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Conceptual Framework – Types, Methodology and Examples

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Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

• Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
• Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
• Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
• Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
• Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
• Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
• Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
• Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
• Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
• Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
• Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

• In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
• In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
• In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
• In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
• In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

• Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
• Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
• Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
• Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
• Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

• Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
• Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
• Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
• Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

• To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
• To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
• To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
• To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
• To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

• Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
• Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
• Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
• Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
• Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
• Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

• Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
• Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
• Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
• Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
• Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
• Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

• Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
• Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
• Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
• Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
• Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
• Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

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.

Table of contents

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 .

• The expected cause, ‘hours of study’, is the independent variable (the predictor, or explanatory variable)
• The expected effect, ‘exam score’, is the dependent variable (the response, or outcome variable).

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 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.

Mediating 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.

Moderator vs mediator

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:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

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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What is a Conceptual Framework?

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format.

Updated on August 28, 2023

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research.  Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results. 

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them. 

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc. 

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same. 

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research. 

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

• Selecting the appropriate research questions
• Defining the process variables (dependent, independent, and others)
• Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables. 

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research. 

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

• Provides the data for molecular docking studies to identify suitable target proteins
• Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration. 

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research. 

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm

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How to Use a Conceptual Framework for Better Research

A conceptual framework in research is not just a tool but a vital roadmap that guides the entire research process. It integrates various theories, assumptions, and beliefs to provide a structured approach to research. By defining a conceptual framework, researchers can focus their inquiries and clarify their hypotheses, leading to more effective and meaningful research outcomes.

What is a Conceptual Framework?

A conceptual framework is essentially an analytical tool that combines concepts and sets them within an appropriate theoretical structure. It serves as a lens through which researchers view the complexities of the real world. The importance of a conceptual framework lies in its ability to serve as a guide, helping researchers to not only visualize but also systematically approach their study.

Key Components and to be Analyzed During Research

• Theories: These are the underlying principles that guide the hypotheses and assumptions of the research.
• Assumptions: These are the accepted truths that are not tested within the scope of the research but are essential for framing the study.
• Beliefs: These often reflect the subjective viewpoints that may influence the interpretation of data.
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Together, these components help to define the conceptual framework that directs the research towards its ultimate goal. This structured approach not only improves clarity but also enhances the validity and reliability of the research outcomes. By using a conceptual framework, researchers can avoid common pitfalls and focus on essential variables and relationships.

For practical examples and to see how different frameworks can be applied in various research scenarios, you can Explore Conceptual Framework Examples .

Different Types of Conceptual Frameworks Used in Research

Understanding the various types of conceptual frameworks is crucial for researchers aiming to align their studies with the most effective structure. Conceptual frameworks in research vary primarily between theoretical and operational frameworks, each serving distinct purposes and suiting different research methodologies.

Theoretical vs Operational Frameworks

Theoretical frameworks are built upon existing theories and literature, providing a broad and abstract understanding of the research topic. They help in forming the basis of the study by linking the research to already established scholarly works. On the other hand, operational frameworks are more practical, focusing on how the study’s theories will be tested through specific procedures and variables.

• Theoretical frameworks are ideal for exploratory studies and can help in understanding complex phenomena.
• Operational frameworks suit studies requiring precise measurement and data analysis.

Choosing the Right Framework

Selecting the appropriate conceptual framework is pivotal for the success of a research project. It involves matching the research questions with the framework that best addresses the methodological needs of the study. For instance, a theoretical framework might be chosen for studies that aim to generate new theories, while an operational framework would be better suited for testing specific hypotheses.

Benefits of choosing the right framework include enhanced clarity, better alignment with research goals, and improved validity of research outcomes. Tools like Table Chart Maker can be instrumental in visually comparing the strengths and weaknesses of different frameworks, aiding in this crucial decision-making process.

Real-World Examples of Conceptual Frameworks in Research

Understanding the practical application of conceptual frameworks in research can significantly enhance the clarity and effectiveness of your studies. Here, we explore several real-world case studies that demonstrate the pivotal role of conceptual frameworks in achieving robust research conclusions.

• Healthcare Research: In a study examining the impact of lifestyle choices on chronic diseases, researchers used a conceptual framework to link dietary habits, exercise, and genetic predispositions. This framework helped in identifying key variables and their interrelations, leading to more targeted interventions.
• Educational Development: Educational theorists often employ conceptual frameworks to explore the dynamics between teaching methods and student learning outcomes. One notable study mapped out the influences of digital tools on learning engagement, providing insights that shaped educational policies.
• Environmental Policy: Conceptual frameworks have been crucial in environmental research, particularly in studies on climate change adaptation. By framing the relationships between human activity, ecological changes, and policy responses, researchers have been able to propose more effective sustainability strategies.

Adapting conceptual frameworks based on evolving research data is also critical. As new information becomes available, it’s essential to revisit and adjust the framework to maintain its relevance and accuracy, ensuring that the research remains aligned with real-world conditions.

For those looking to visualize and better comprehend their research frameworks, Graphic Organizers for Conceptual Frameworks can be an invaluable tool. These organizers help in structuring and presenting research findings clearly, enhancing both the process and the presentation of your research.

Step-by-Step Guide to Creating Your Own Conceptual Framework

Creating a conceptual framework is a critical step in structuring your research to ensure clarity and focus. This guide will walk you through the process of building a robust framework, from identifying key concepts to refining your approach as your research evolves.

Building Blocks of a Conceptual Framework

• Identify and Define Main Concepts and Variables: Start by clearly identifying the main concepts, variables, and their relationships that will form the basis of your research. This could include defining key terms and establishing the scope of your study.
• Develop a Hypothesis or Primary Research Question: Formulate a central hypothesis or question that guides the direction of your research. This will serve as the foundation upon which your conceptual framework is built.
• Link Theories and Concepts Logically: Connect your identified concepts and variables with existing theories to create a coherent structure. This logical linking helps in forming a strong theoretical base for your research.

Visualizing and Refining Your Framework

Using visual tools can significantly enhance the clarity and effectiveness of your conceptual framework. Decision Tree Templates for Conceptual Frameworks can be particularly useful in mapping out the relationships between variables and hypotheses.

Map Your Framework: Utilize tools like Creately’s visual canvas to diagram your framework. This visual representation helps in identifying gaps or overlaps in your framework and provides a clear overview of your research structure.

Analyze and Refine: As your research progresses, continuously evaluate and refine your framework. Adjustments may be necessary as new data comes to light or as initial assumptions are challenged.

By following these steps, you can ensure that your conceptual framework is not only well-defined but also adaptable to the changing dynamics of your research.

Practical Tips for Utilizing Conceptual Frameworks in Research

Effectively utilizing a conceptual framework in research not only streamlines the process but also enhances the clarity and coherence of your findings. Here are some practical tips to maximize the use of conceptual frameworks in your research endeavors.

• Setting Clear Research Goals: Begin by defining precise objectives that are aligned with your research questions. This clarity will guide your entire research process, ensuring that every step you take is purposeful and directly contributes to your overall study aims. \
• Maintaining Focus and Coherence: Throughout the research, consistently refer back to your conceptual framework to maintain focus. This will help in keeping your research aligned with the initial goals and prevent deviations that could dilute the effectiveness of your findings.
• Data Analysis and Interpretation: Use your conceptual framework as a lens through which to view and interpret data. This approach ensures that the data analysis is not only systematic but also meaningful in the context of your research objectives. For more insights, explore Research Data Analysis Methods .
• Presenting Research Findings: When it comes time to present your findings, structure your presentation around the conceptual framework . This will help your audience understand the logical flow of your research and how each part contributes to the whole.
• Avoiding Common Pitfalls: Be vigilant about common errors such as overcomplicating the framework or misaligning the research methods with the framework’s structure. Keeping it simple and aligned ensures that the framework effectively supports your research.

By adhering to these tips and utilizing tools like 7 Essential Visual Tools for Social Work Assessment , researchers can ensure that their conceptual frameworks are not only robust but also practically applicable in their studies.

How Creately Enhances the Creation and Use of Conceptual Frameworks

Creating a robust conceptual framework is pivotal for effective research, and Creately’s suite of visual tools offers unparalleled support in this endeavor. By leveraging Creately’s features, researchers can visualize, organize, and analyze their research frameworks more efficiently.

• Visual Mapping of Research Plans: Creately’s infinite visual canvas allows researchers to map out their entire research plan visually. This helps in understanding the complex relationships between different research variables and theories, enhancing the clarity and effectiveness of the research process.
• Brainstorming with Mind Maps: Using Mind Mapping Software , researchers can generate and organize ideas dynamically. Creately’s intelligent formatting helps in brainstorming sessions, making it easier to explore multiple topics or delve deeply into specific concepts.
• Centralized Data Management: Creately enables the importation of data from multiple sources, which can be integrated into the visual research framework. This centralization aids in maintaining a cohesive and comprehensive overview of all research elements, ensuring that no critical information is overlooked.
• Communication and Collaboration: The platform supports real-time collaboration, allowing teams to work together seamlessly, regardless of their physical location. This feature is crucial for research teams spread across different geographies, facilitating effective communication and iterative feedback throughout the research process.

Moreover, the ability t Explore Conceptual Framework Examples directly within Creately inspires researchers by providing practical templates and examples that can be customized to suit specific research needs. This not only saves time but also enhances the quality of the conceptual framework developed.

In conclusion, Creately’s tools for creating and managing conceptual frameworks are indispensable for researchers aiming to achieve clear, structured, and impactful research outcomes.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

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Chiraag George is a communication specialist here at Creately. He is a marketing junkie that is fascinated by how brands occupy consumer mind space. A lover of all things tech, he writes a lot about the intersection of technology, branding and culture at large.

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

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

Understanding conceptual frameworks

Selecting and developing your framework, variables in a conceptual framework.

• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Conceptual framework: Definition and theory

Theoretical and conceptual frameworks ultimately go hand in hand, but while there is significant overlap with theoretical perspectives and theoretical frameworks, understanding the essential differences is important when designing your research project.

Let's explore the idea of a conceptual framework, provide a few common examples, and discuss how to choose a framework for your study. Keep in mind that a conceptual framework will differ from a theoretical framework and that we will explore these differences in the next section.

In this section, we'll delve into the intricacies of conceptual frameworks and their role in qualitative research . They are essentially the scaffolding on which you hang your research questions and analysis . They define the concepts that you'll study and articulate the relationships among them.

Developing conceptual frameworks in research

At the most basic level, a conceptual framework is a visual or written product that explains, either graphically or in narrative form, the main things to be studied, the key factors, variables, or constructs, and any presumed relationships among them. It acts as a road map guiding the course of your research, directing what will be studied, and helping to organize and analyze the data.

The purpose of a conceptual framework

A conceptual framework serves multiple functions in a research project. It helps in clarifying the research problem and purpose, assists in refining the research questions, and guides the data collection and analysis process. It's the tool that ties all aspects of the study together, offering a coherent perspective for the researcher and readers to understand the research more holistically.

Relation between theoretical perspectives and conceptual frameworks

Theoretical perspectives offer overarching philosophies and assumptions that guide the research process, while conceptual frameworks are the specific devices that are derived from these perspectives to operationalize the study. If a theoretical perspective is the broad philosophical underpinning, a conceptual framework is a pragmatic approach that puts that philosophy into practice in the context of the study.

For instance, if you're working from a feminist theoretical perspective, your conceptual framework might involve specific constructs like gender roles, power dynamics , and societal norms, as well as the relationships between these constructs. The conceptual framework would be the lens through which you examine and interpret your data, guided by your theoretical perspective.

Critical theory

Critical theory is a theoretical perspective that seeks to confront social, historical, and ideological forces and structures that produce and constrain social problems. The corresponding conceptual framework might focus on constructs like power relations, historical context, and societal structures. For instance, a study on income inequality might have a conceptual framework involving constructs of socioeconomic status, institutional policies, and the distribution of resources.

Feminist theory

Feminist theory emphasizes the societal roles of gender and power relationships. A conceptual framework derived from this theory might involve constructs like gender roles, power dynamics, and societal norms. In a study about gender representation in media, a feminist conceptual framework could involve constructs such as stereotyping, representation, and societal expectations of gender.

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Choosing and developing your conceptual framework is a pivotal process in your research design. This framework will help guide your study, inform your methodology , and shape your analysis .

Factors to consider when choosing a framework

Your conceptual framework should be derived from and align with your chosen theoretical perspective , but there are other considerations as well. It should resonate with your research question , problem, or purpose and be applicable to the specific context or population you are studying. You should also consider the feasibility of operationalizing the constructs in your framework.

When selecting a conceptual framework, consider the following questions:

1. How does this framework relate to my research topic? 2. Can I use this framework to effectively address my research question(s)? 3. Does this framework resonate with the population and context I'm studying? 4. Can the constructs in this framework be feasibly operationalized in my study?

Steps in developing a conceptual framework

Developing your conceptual framework involves a few key steps:

1. Identify key constructs: Based on your theoretical perspective and research question(s) , what are the main constructs or variables that you need to explore in your study? 2. Clarify relationships among constructs: How do these constructs relate to each other? Are there presumed causal relationships, correlations, or other types of associations? 3. Define each construct: Clearly define what each construct means in the context of your study. This might also involve operationalizing each construct or defining the indicators you will use to measure or identify each construct. 4. Create a visual representation : It is often extremely helpful to create a visual representation of your conceptual framework to illustrate the constructs and their relationships. Map out the relationships among constructs to develop a holistic understanding of what you want to study.

Remember, your conceptual framework is not set in stone. You can start creating your conceptual framework based on your literature review and your own critical reflections. As you proceed with your study, you might need to refine or adapt your conceptual framework based on what you're learning from your data. Developing a robust framework is an iterative process that requires critical thinking, creativity, and flexibility.

A strong conceptual framework includes variables that refer to the constructs or characteristics that are being studied. They are the building blocks of your research study. It might be helpful to think about how the variables in your conceptual framework could be categorized as independent and dependent variables, which respectively influence and are influenced within the research study.

Independent variables and dependent variables

An independent variable is the characteristic or condition that is manipulated or selected by the researcher to determine its effect on the dependent variable. For example, in a study exploring the impact of classroom size on student engagement, classroom size would be the independent variable.

The dependent variable is the main outcome that the researcher is interested in studying or explaining. In the example given above, student engagement would be the dependent variable, as it's the outcome being observed for any changes in response to the independent variable (classroom size). In essence, defining these variables can help you identify the cause-and-effect relationships in your study. While it might be difficult to know beforehand exactly which variables will be important and how they relate to one another, this is a helpful thought exercise to flesh out potential relationships among variables you may want to study.

Relationships among variables

Within a conceptual framework, the dependent and independent variables are listed in addition to their proposed relationships to each other. The ways in which these variables influence one another form the crux of the propositions or assumptions that guide your research.

In a conceptual framework based on the theoretical perspective of constructivism, for instance, the independent variable might be a teaching method (as constructivists would argue that methods of instruction can shape learning), and the dependent variable could be the depth of student understanding. The proposed relationship between these variables might be that student-centered teaching methods lead to a deeper understanding, which would guide the data collection and analysis such that this proposition could be explored.

However, it is important to note that the terminology of independent and dependent variables is more typical of quantitative research , in which independent and dependent variables are operationalized in hypotheses that will be tested based on pre-established theory. In qualitative research , the relationships between variables are more fluid and open-ended because the focus is often more on understanding the phenomenon as a whole and building a contextualized understanding of the research problem. This can involve including new or unexpected variables and interrelationships that emerge during the study, thus extending previous theory or understanding that didn’t initially predict these relationships.

Thus, in your conceptual framework, rather than solely focusing on identifying independent and dependent variables, consider how various factors interact and influence one another within the context of your study. Your conceptual framework should provide a holistic picture of the complexity of the phenomenon you are studying.

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Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

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 both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

Supplementary Material

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What is a Conceptual Framework? Expounded Definition and Five Purposes

What is a conceptual framework? I gathered all the relevant definitions of a conceptual framework, what it does in the research process, and extracted the key ideas to give you an extensive and easily understood picture of the meaning and purposes of this often sought concept in research.

I find it necessary to gather all the information written about a conceptual framework’s meaning in working out a study. The definitions given in this article aims to provide you a solid foundation on this critical aspect of the research paper.

What is a Conceptual Framework?

I define the conceptual framework based on a review of relevant articles using the keyword “What is a conceptual framework ?” This definition integrates the ideas presented in the articles of the first two pages of a Google search. In this context, relevant articles mean the definitions come from academic institutions, recognized scientific publications, and academicians.

In keeping with the mission of this site to translate complex scientific concepts to digestible bits, I extracted the main thoughts, grouped them according to their salient features, and expounded on them for clarity without sacrificing the essence of the original articles’ ideas. I adopted this approach to avoid plagiarism and minimize jargon that makes the understanding of the conceptual framework fuzzy to a beginning researcher .

The effort to explain this concept took time, imagination, and reflective thinking to present the complex discussion in articles coming from academicians into plain language. Hence, I use visual representations of the key takeaways of the readings for those who could not easily grasp the textual version.

I provide three useful landmarks or seminal references related to this topic in this post’s reference section. These landmark or well-cited literature (thousands of researchers cited the papers) were written by seasoned scientists, namely Brunswick (1952), Greene et al. (1989), and Jabareen (2009). I linked the last one as it is available online for you to read, whereas the other two are not. You need to pay to read them. These references illustrate examples of how a conceptual framework guides current thinking on different subjects of interest.

Definition of a Conceptual Framework

Conceptual frameworks are products of a person’s reasoning.

A conceptual framework is a product of a researcher’s reasoning or tentative conclusion. The concepts arrived at are based on a literature review where evidence is still incomplete, or theories arrived at are inadequate. It is a synthesis of interlinked concepts that provide a comprehensive understanding of a phenomenon (see how  mind mapping  can help with this concern). 

Although the basis for such conclusions is tentative, an explanation of a phenomenon comes up as the formula to explain the order of things. That’s where the hypothesis comes in.

The concepts presented are integrated

The concepts presented in a conceptual framework interlock with each other, serving as a firm foundation for the hypothesis. Thus, we call it a  framework , as it supports an argument that could stir up healthy and productive conversations, controversies, debates, or innovations to fuel economic growth.

How long that foundation will stand, time will tell. If it stands the test of time as new findings unravel, and it gets supported by other studies, then it becomes a theory. This approach follows the theory of knowledge termed as epistemology , which distinguishes justified belief or plain opinion.

In justified belief, evidence is a crucial element. If you can support your idea (initially a conjecture or inference) with sufficient evidence, your argument holds.

I present a concept map below to illustrate the points made in this discussion.

At the master’s degree level, your research confirms, validates, or supports an existing theory that is supported by other researchers’ hypotheses. At the doctoral degree level, your research leads to the creation of a new theory . The development of your concept arises from reflective thinking after reviewing the literature and drawing from your experiences, which includes keen observations made on a phenomenon.

Take note that the concepts presented in the conceptual framework support or interact with each other. The interaction expresses an idea, a difference perhaps, similarity, or a relationship governed by an inner philosophy—the theory that the framework purports, confirms or supports.

Hence, what you do in conceptualization is to integrate your knowledge based on experiences and others’ experiences (other researchers). It is a synthesis of previous findings plus experiences through reflective thinking.

It is an objective interpretation

This approach also says that whatever conceptual framework was arrived at is subject to the person’s biases. But excellent research requires an objective mind in dealing with issues.

Thus, if you are a good researcher , you must view the findings as they are, without favoring one perspective. Good research practice means that you MUST NOT deliberately select only those literature that supports your position. Instead, you objectively examine your ideas in the light of what has already been discovered. It must also consider the other researchers’ opposing views to achieve balance.

To sum it all up, the conceptual framework is a researcher’s  constructs  in a backdrop of broad theories that operate at a higher plane and stand on firm grounds, supporting a philosophy on how things work in reality.

Before this goes out of hand, and the discussion becomes more complex, let me strengthen your understanding by shifting to the purposes of a conceptual framework. I enumerate five of them below.

Five Purposes of the Conceptual Framework

1. the conceptual framework is an analytical tool.

The conceptual framework serves as a tool in analyzing the state of things (variables or concepts) and their interactions for a comprehensive understanding of a phenomenon. The purpose of the conceptual framework is to guide your thinking when the data comes in.

2. Guides the Research Hypothesis and Methodology

Another purpose of the conceptual framework is to identify the variables of the study. Identification of the conceptual framework variables provides information on the appropriate statistical tools to find out differences or relationships between variables. If you are conducting a qualitative study, you can find the proper methodology for a subjective, in-depth discussion of how the concepts relate to each other.

Jabareen (2009) proposes that the representation is better referred to as MODELS whenever variables or measurable qualities are used. That is when quantification is made. If non-quantifiable concepts are used, then conceptual framework is the appropriate term.

3. Illustrates the Research Approach

The conceptual framework is usually accompanied by a visual representation of the interaction or relationships of the different components called the  research paradigm . While developing your conceptual framework, you can make some changes to the paradigm to adequately represent the conceptual framework’s textual explanation.

4. Generates New Interpretations

The fourth purpose of the conceptual framework is to generate new interpretations. A systematic synthesis of the study’s findings using a conceptual framework generates new interpretations of existing theories. When several studies are combined and synthesized, such as when doing a meta-analysis, the results will reveal novel insights that are far superior to one arrived at in a single study.

Here is an interesting three-minute video on what is a meta-analysis.

5. It reveals the gap in knowledge

Finally, the purpose of the conceptual framework is to reveal the gaps in knowledge. If the findings show that some aspects of the phenomenon have not been explored or well understood, then that’s where you come in. You have found that elusive “research gap,” which your study can help address or resolve. That’s the beauty of having a conceptual framework ready at hand.

At this point, the conceptual framework’s definition and purposes should have been hemmed in for keeps in your head. Be armed with this knowledge and work your way out in the academic world.

Reading a lot of relevant literature on your chosen topic and exposing yourself to the issue you are interested in makes the conceptual framework development a much more significant and accurate representation of reality.

Brunswik, E. (1952). The conceptual framework of psychology. Psychological Bulletin, 49(6), 654-656.

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational evaluation and policy analysis, 11(3), 255-274.

Jabareen, Y. (2009). Building a conceptual framework: philosophy, definitions, and procedure. International journal of qualitative methods, 8(4), 49-62.

© 2020 November 5 P. A. Regoniel

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About the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

• Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

20 Comments

Thank you for giving a valuable lesson

good thanks!

VERY INSIGHTFUL

thanks for given very interested understand about both theoritical and conceptual framework

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

good, fantastic

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Thanks for shedding light on these two t opics. Much clearer in my head now.

Simple and clear!

The differences between the two topics was well explained, thank you very much!

Thank you great insight

Superb. Thank you so much.

Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

I am really grateful I found this website. This is very helpful for an MPA student like myself.

I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

I’m well inform about these two concepts in research. Thanks

I found this really helpful. It is well explained. Thank you.

very clear and useful. information important at start of research!!

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Home Market Research

Conceptual Research: Definition, Framework, Example and Advantages

Conceptual Research: Definition

Conceptual research is defined as a methodology wherein research is conducted by observing and analyzing already present information on a given topic. Conceptual research doesn’t involve conducting any practical experiments. It is related to abstract concepts or ideas. Philosophers have long used conceptual research to develop new theories or interpret existing theories in a different light.

For example, Copernicus used conceptual research to come up with the concepts of stellar constellations based on his observations of the universe. Down the line, Galileo simplified Copernicus’s research by making his own conceptual observations which gave rise to more experimental research and confirmed the predictions made at that time.

The most famous example of conceptual research is Sir Issac Newton. He observed his surroundings to conceptualize and develop theories about gravitation and motion.

Einstein is widely known and appreciated for his work on conceptual research. Although his theories were based on conceptual observations, Einstein also proposed experiments to come up with theories to test the conceptual research.

Nowadays, conceptual research is used to answer business questions and solve real-world problems. Researchers use analytical research tools called conceptual frameworks to make conceptual distinctions and organize ideas required for research purposes.

Conceptual Research Framework

Conceptual research framework constitutes of a researcher’s combination of previous research and associated work and explains the occurring phenomenon. It systematically explains the actions needed in the course of the research study based on the knowledge obtained from other ongoing research and other researchers’ points of view on the subject matter.

Here is a stepwise guide on how to create the conceptual research framework:

01. Choose the topic for research

Before you start working on collecting any research material, you should have decided on your topic for research. It is important that the topic is selected beforehand and should be within your field of specialization.

02. Collect relevant literature

Once you have narrowed down a topic, it is time to collect relevant information about it. This is an important step, and much of your research is dependent on this particular step, as conceptual research is mostly based on information obtained from previous research. Here collecting relevant literature and information is the key to successfully completing research.

The material that you should preferably use is scientific journals , research papers published by well-known scientists , and similar material. There is a lot of information available on the internet and in public libraries as well. All the information that you find on the internet may not be relevant or true. So before you use the information, make sure you verify it.  

03. Identify specific variables

Identify the specific variables that are related to the research study you want to conduct. These variables can give your research a new scope and can also help you identify how these can be related to your research design . For example, consider hypothetically you want to conduct research about the occurrence of cancer in married women. Here the two variables that you will be concentrating on are married women and cancer.

While collecting relevant literature, you understand that the spread of cancer is more aggressive in married women who are beyond 40 years of age. Here there is a third variable which is age, and this is a relevant variable that can affect the end result of your research.  

04. Generate the framework

In this step, you start building the required framework using the mix of variables from the scientific articles and other relevant materials. The research problem statement in your research becomes the research framework. Your attempt to start answering the question becomes the basis of your research study. The study is carried out to reduce the knowledge gap and make available more relevant and correct information.

Example of Conceptual Research Framework

Thesis statement/ Purpose of research: Chronic exposure to sunlight can lead to precancerous (actinic keratosis), cancerous (basal cell carcinoma, squamous cell carcinoma, and melanoma), and even skin lesions (caused by loss of skin’s immune function) in women over 40 years of age.

The study claims that constant exposure to sunlight can cause the precancerous condition and can eventually lead to cancer and other skin abnormalities. Those affected by these experience symptoms like fatigue, fine or coarse wrinkles, discoloration of the skin, freckles, and a burning sensation in the more exposed areas.

Note that in this study, there are two variables associated- cancer and women over 40 years in the African subcontinent. But one is a dependent variable (women over 40 years, in the African subcontinent), and the other is an independent variable (cancer). Cumulative exposure to the sun till the age of 18 years can lead to symptoms similar to skin cancer. If this is not taken care of, there are chances that cancer can spread entirely.

Assuming that the other factors are constant during the research period, it will be possible to correlate the two variables and thus confirm that, indeed, chronic exposure to sunlight causes cancer in women over the age of 40 in the African subcontinent. Further, correlational research can verify this association further.

Advantages of Conceptual Research

1. Conceptual research mainly focuses on the concept of the research or the theory that explains a phenomenon. What causes the phenomenon, what are its building blocks, and so on? It’s research based on pen and paper.

2. This type of research heavily relies on previously conducted studies; no form of experiment is conducted, which saves time, effort, and resources. More relevant information can be generated by conducting conceptual research.

3. Conceptual research is considered the most convenient form of research. In this type of research, if the conceptual framework is ready, only relevant information and literature need to be sorted.

QuestionPro for Conceptual Research

QuestionPro offers readily available conceptual frameworks. These frameworks can be used to research consumer trust, customer satisfaction (CSAT) , product evaluations, etc. You can select from a wide range of templates question types, and examples curated by expert researchers.

We also help you decide which conceptual framework might be best suited for your specific situation.

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Scientific Research and Methodology

2.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester. A conceptual definition would describe what is meant by ‘stress.’ An operational definition would describe how the ‘stress’ would be measured.

Sometimes the definitions themselves aren’t important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an ‘adult’ in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 2.2 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful… it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

… a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)… [ Note : This table includes symptoms such as ‘convulsion,’ ‘clearly dazed,’ etc.]; An abnormal post game, same day assessment…; An abnormal 36–48 h assessment…; The presence of clinical suspicion by the treating doctor at any time…

Example 2.3 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 2.4 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen et al. 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of ‘snacking’:

…an eating occasion that occurred between meals based on time of day. — Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the ‘pace of life’ and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for ‘pace of life’ (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave ‘change for two $20 bills or [gave] two $20 bills for change.’
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure ‘pace of life,’ of course. Nonetheless, the researchers found that, compared to people on the West Coast,

… people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch… — Levine ( 1990 ) (p. 455)

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Defining The Conceptual Framework

Making a conceptual framework, conceptual framework for dmft students, conceptual framework guide, example frameworks, additional framework resources.

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What is it?

• The researcher’s understanding/hypothesis/exploration of either an existing framework/model or how existing concepts come together to inform a particular problem. Shows the reader how different elements come together to facilitate research and a clear understanding of results.
• Informs the research questions/methodology (problem statement drives framework drives RQs drives methodology)
• A tool (linked concepts) to help facilitate the understanding of the relationship among concepts or variables in relation to the real-world. Each concept is linked to frame the project in question.
• Falls inside of a larger theoretical framework (theoretical framework = explains the why and how of a particular phenomenon within a particular body of literature).
• Can be a graphic or a narrative – but should always be explained and cited
• Can be made up of theories and concepts

What does it do?

• Explains or predicts the way key concepts/variables will come together to inform the problem/phenomenon
• Gives the study direction/parameters
• Helps the researcher organize ideas and clarify concepts
• Introduces your research and how it will advance your field of practice. A conceptual framework should include concepts applicable to the field of study. These can be in the field or neighboring fields – as long as important details are captured and the framework is relevant to the problem. (alignment)

What should be in it?

• Variables, concepts, theories, and/or parts of other existing frameworks

How to make a conceptual framework

• With a topic in mind, go to the body of literature and start identifying the key concepts used by other studies. Figure out what’s been done by other researchers, and what needs to be done (either find a specific call to action outlined in the literature or make sure your proposed problem has yet to be studied in your specific setting). Use what you find needs to be done to either support a pre-identified problem or craft a general problem for study. Only rely on scholarly sources for this part of your research.
• Begin to pull out variables, concepts, theories, and existing frameworks explained in the relevant literature.
• If you’re building a framework, start thinking about how some of those variables, concepts, theories, and facets of existing frameworks come together to shape your problem. The problem could be a situational condition that requires a scholar-practitioner approach, the result of a practical need, or an opportunity to further an applicational study, project, or research. Remember, if the answer to your specific problem exists, you don’t need to conduct the study.
• The actionable research you’d like to conduct will help shape what you include in your framework. Sketch the flow of your Applied Doctoral Project from start to finish and decide which variables are truly the best fit for your research.
• Create a graphic representation of your framework (this part is optional, but often helps readers understand the flow of your research) Even if you do a graphic, first write out how the variables could influence your Applied Doctoral Project and introduce your methodology. Remember to use APA formatting in separating the sections of your framework to create a clear understanding of the framework for your reader.
• As you move through your study, you may need to revise your framework.
• Note for qualitative/quantitative research: If doing qualitative, make sure your framework doesn’t include arrow lines, which could imply causal or correlational linkages.
• Conceptural and Theoretical Framework for DMFT Students This document is specific to DMFT students working on a conceptual or theoretical framework for their applied project.
• Conceptual Framework Guide Use this guide to determine the guiding framework for your applied dissertation research.

Let’s say I’ve just taken a job as manager of a failing restaurant. Throughout the first week, I notice the few customers they have are leaving unsatisfied. I need to figure out why and turn the establishment into a thriving restaurant. I get permission from the owner to do a study to figure out exactly what we need to do to raise levels of customer satisfaction. Since I have a specific problem and want to make sure my research produces valid results, I go to the literature to find out what others are finding about customer satisfaction in the food service industry. This particular restaurant is vegan focused – and my search of the literature doesn’t say anything specific about how to increase customer service in a vegan atmosphere, so I know this research needs to be done.

I find out there are different types of satisfaction across other genres of the food service industry, and the one I’m interested in is cumulative customer satisfaction. I then decide based on what I’m seeing in the literature that my definition of customer satisfaction is the way perception, evaluation, and psychological reaction to perception and evaluation of both tangible and intangible elements of the dining experience come together to inform customer expectations. Essentially, customer expectations inform customer satisfaction.

I then find across the literature many variables could be significant in determining customer satisfaction. Because the following keep appearing, they are the ones I choose to include in my framework: price, service, branding (branched out to include physical environment and promotion), and taste. I also learn by reading the literature, satisfaction can vary between genders – so I want to make sure to also collect demographic information in my survey. Gender, age, profession, and number of children are a few demographic variables I understand would be helpful to include based on my extensive literature review.

Note: this is a quantitative study. I’m including all variables in this study, and the variables I am testing are my independent variables. Here I’m working to see how each of the independent variables influences (or not) my dependent variable, customer satisfaction. If you are interested in qualitative study, read on for an example of how to make the same framework qualitative in nature.

Also note: when you create your framework, you’ll need to cite each facet of your framework. Tell the reader where you got everything you’re including. Not only is it in compliance with APA formatting, but also it raises your credibility as a researcher. Once you’ve built the narrative around your framework, you may also want to create a visual for your reader.

See below for one example of how to illustrate your framework:

If you’re interested in a qualitative study, be sure to omit arrows and other notations inferring statistical analysis. The only time it would be inappropriate to include a framework in qualitative study is in a grounded theory study, which is not something you’ll do in an applied doctoral study.

A visual example of a qualitative framework is below:

Some additional helpful resources in constructing a conceptual framework for study:

• Problem Statement, Conceptual Framework, and Research Question. McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg
• Building a Conceptual Framework: Philosophy, Definitions, and Procedure
• https://www.scribbr.com/dissertation/conceptual-framework/
• https://www.projectguru.in/developing-conceptual-framework-in-a-research-paper/

Conceptual Framework Research

A conceptual framework is a synthetization of interrelated components and variables which help in solving a real-world problem. It is the final lens used for viewing the deductive resolution of an identified issue (Imenda, 2014). The development of a conceptual framework begins with a deductive assumption that a problem exists, and the application of processes, procedures, functional approach, models, or theory may be used for problem resolution (Zackoff et al., 2019). The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013). In applied research the application of theory in problem solving focuses on how theory in conjunction with practice (applied action) and procedures (functional approach) frames vision, thinking, and action towards problem resolution. The inclusion of theory in a conceptual framework is not focused on validation or devaluation of applied theories. A concise way of viewing the conceptual framework is a list of understood fact-based conditions that presents the researcher’s prescribed thinking for solving the identified problem. These conditions provide a methodological rationale of interrelated ideas and approaches for beginning, executing, and defining the outcome of problem resolution efforts (Leshem & Trafford, 2007).

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). Just as with traditional research, a theory does not or cannot be expected to explain all phenomenal conditions, a conceptual framework is not a random identification of disparate ideas meant to incase a problem. Instead it is a means of identifying and constructing for the researcher and reader alike an epistemological mindset and a functional worldview approach to the identified problem.

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Imenda, S. (2014). Is There a Conceptual Difference between Theoretical and Conceptual Frameworks? Sosyal Bilimler Dergisi/Journal of Social Sciences, 38(2), 185.

Leshem, S., & Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education & Teaching International, 44(1), 93–105. https://doi-org.proxy1.ncu.edu/10.1080/14703290601081407

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

Zackoff, M. W., Real, F. J., Klein, M. D., Abramson, E. L., Li, S.-T. T., & Gusic, M. E. (2019). Enhancing Educational Scholarship Through Conceptual Frameworks: A Challenge and Roadmap for Medical Educators . Academic Pediatrics, 19(2), 135–141. https://doi-org.proxy1.ncu.edu/10.1016/j.acap.2018.08.003

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Open Access

Peer-reviewed

Research Article

Development of a conceptual framework for defining trial efficiency

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Wolfson Institute of Population Health, Queen Mary University of London, London, England, United Kingdom

Roles Conceptualization, Methodology, Supervision, Validation

Affiliation Usher Institute, Asthma UK Centre for Applied Research, The University of Edinburgh, Edinburgh, Scotland, United Kingdom

• Charis Xuan Xie, 
• Anna De Simoni, 
• Sandra Eldridge, 
• Hilary Pinnock, 
• Clare Relton

• Published: May 23, 2024
• https://doi.org/10.1371/journal.pone.0304187
• Peer Review
• Reader Comments

Globally, there is a growing focus on efficient trials, yet numerous interpretations have emerged, suggesting a significant heterogeneity in understanding “efficiency” within the trial context. Therefore in this study, we aimed to dissect the multifaceted nature of trial efficiency by establishing a comprehensive conceptual framework for its definition.

To collate diverse perspectives regarding trial efficiency and to achieve consensus on a conceptual framework for defining trial efficiency.

From July 2022 to July 2023, we undertook a literature review to identify various terms that have been used to define trial efficiency. We then conducted a modified e-Delphi study, comprising an exploratory open round and a subsequent scoring round to refine and validate the identified items. We recruited a wide range of experts in the global trial community including trialists, funders, sponsors, journal editors and members of the public. Consensus was defined as items rated “without disagreement”, measured by the inter-percentile range adjusted for symmetry through the UCLA/RAND approach.

Seventy-eight studies were identified from a literature review, from which we extracted nine terms related to trial efficiency. We then used review findings as exemplars in the Delphi open round. Forty-nine international experts were recruited to the e-Delphi panel. Open round responses resulted in the refinement of the initial nine terms, which were consequently included in the scoring round. We obtained consensus on all nine items: 1) four constructs that collectively define trial efficiency containing scientific efficiency, operational efficiency, statistical efficiency and economic efficiency; and 2) five essential building blocks for efficient trial comprising trial design, trial process, infrastructure, superstructure, and stakeholders.

Conclusions

This is the first attempt to dissect the concept of trial efficiency into theoretical constructs. Having an agreed definition will allow better trial implementation and facilitate effective communication and decision-making across stakeholders. We also identified essential building blocks that are the cornerstones of an efficient trial. In this pursuit of understanding, we are not only unravelling the complexities of trial efficiency but also laying the groundwork for evaluating the efficiency of an individual trial or a trial system in the future.

Citation: Xie CX, De Simoni A, Eldridge S, Pinnock H, Relton C (2024) Development of a conceptual framework for defining trial efficiency. PLoS ONE 19(5): e0304187. https://doi.org/10.1371/journal.pone.0304187

Editor: Germain Honvo, University of Liege: Universite de Liege, BELGIUM

Received: December 4, 2023; Accepted: May 7, 2024; Published: May 23, 2024

Copyright: © 2024 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its supporting information files.

Funding: CX is funded by the Wellcome Trust (224863/Z/21/Z). URL: https://wellcome.org/ . For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The funder does not play any role in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Worldwide, trial efficiency is a longstanding priority for the pharmaceutical industry [ 1 ], academia and funding bodies [ 2 , 3 ]. In 2004 in the US, the Clinical Trials Working Group of the National Cancer Advisory Board set the goal of improving operational efficiency to facilitate timely and cost-effective trial execution [ 4 ]. In the UK, the National Institute for Health and Care Research offers additional funding to support clinical trial units to advance the design and execution of efficient, innovative research, aiming to provide robust evidence to inform clinical practice and policy [ 5 ]. A recent article in The Lancet Global Health examined the challenges faced by current clinical trial research in low- and middle-income countries, and argued that efficient trials are needed to address research questions related to the increasing burden of non-communicable diseases in a timely and affordable way [ 6 ].

Currently, the concept of efficiency in healthcare trials has been used to refer to accelerated ethical approval [ 6 ], addressing multiple complex questions in a single trial [ 7 ] and with a minimised sample size [ 6 ], trials conducted with shorter duration [ 7 , 8 ], lower costs [ 9 ], and reduced resource requirements [ 10 ]. In addition, existing literature has discussed trial efficiency in terms of operational efficiency [ 11 – 13 ], scientific efficiency [ 11 ], statistical efficiency [ 13 , 14 ], and economic efficiency [ 15 ]. There is significant heterogeneity as to what is meant by efficiency in the context of trials, which may hinder effective communication and decision-making between stakeholders, and compromise the comparability of studies. Therefore, in this study we aimed to develop a conceptual framework for defining trial efficiency and to achieve expert consensus on the framework constructs.

Study design

We undertook a literature review to identify items that define and comprise trial efficiency. We then conducted an e-Delphi study to refine and validate those items and to achieve consensus on the constructs and the building blocks of trial efficiency. The ethics approval was obtained from Queen Mary University of London research ethics committee (QMERC22.316). This study follows the Guidance on Conducting and Reporting Delphi Studies (CREDES) [ 16 ].

Literature review for generating items

Our goal in the literature review was to collate existing discussions on efficiency in the context of trials, including definitions and attributes described as constituting an efficient trial. As discussions specifically focused on this subject are scarce, we included a broad range of study types, such as full trial papers or protocols, editorials, and opinion pieces that discussed trial efficiency. We considered all types of human trials evaluating medical, surgical, or behavioural interventions, including efficacy trials, effectiveness trials, and implementation trials. The search was limited to English-language articles, and there was no restriction on publication dates. To carry out the review, we searched MEDLINE (via Ovid) database, for terms such as ’trial’ and ’efficien*’ in article titles and keywords. As ’efficiency’ is a common word in literature, we searched for these two keywords only within article titles (rather than within the abstracts) ensuring the results’ relevance to the discussion of trial efficiency. The detailed inclusion and exclusion criteria are listed in S1 Table .

Panel selection and recruitment.

The aim was to recruit a diverse panel of experts from the trial community, encompassing a range of roles and perspectives. This included international researchers identified through the literature review, colleagues who are part of professional trial networks such as UK trial managers’ network, representatives from funding bodies, journal editors, and members of the public who have been involved in trials. Purposive sampling and snowball sampling methods were then used to identify additional participants. We approached those participants with known contact details by individual emails generated through Clinvivo [ 17 ], while for colleagues within professional networks, where we didn’t have individual contact details, we sent a generic recruitment email to the network’s mailing list. Recruitment began in November 2022 and continued until March 2023. Written informed consent was obtained online through the Clinvivo Delphi system.

Data collection.

We opted for two rounds of data collection because consensus was achieved by the end of the second round. These rounds were preceded by a pilot round to test the feasibility of the open round.

Pilot test . We pilot tested the feasibility of the open round questionnaire amongst colleagues with diverse experience in trial design and conduct at the Pragmatic Clinical Trial Unit of Queen Mary University of London. This provided valuable feedback on the clarity of the questions, the appropriateness of the response options, and the overall structure of the questionnaire. Based on the feedback received during the pilot testing, we made revisions and refinements to the questionnaire to enhance its usability.

Open round . In the open round, we invited panellists to share their thoughts on 1) their understanding of trial efficiency and 2) the most efficient or inefficient aspects they have encountered in the trials they have conducted or in which they have participated. These questions were designed as free-text to encourage detailed, narrative responses. To gain insights into the participants’ backgrounds, we collected information on countries of residence, and roles within the trials (see S1 File for the questionnaire). This open round allowed us to gather diverse viewpoints and experiences related to trial efficiency which contributed to the development of a comprehensive set of items for ranking in the subsequent round. The data collection for this round took place over four weeks, with reminder emails sent to participants after the second and third weeks.

Scoring round . Panel members from open round were emailed a link to the second questionnaire. They were asked to rate the importance of the proposed items on a scale of 1 to 9 (1: not at all important to 9: critically important). At the end of each question, there was a free text space for any comments they wished to share. The scoring round data collection spanned four weeks with weekly reminders to participants.

Data analysis and consensus.

Descriptive statistics were used to analyse quantitative demographics and thematic analysis was used to summarise free text responses from both Delphi rounds. To assess disagreement and appropriateness, we used the Research ANd Development (RAND)/ University of California Los Angeles (UCLA) appropriateness method [ 18 ]. It involves calculating the median score, the inter-percentile range (IPR) (30th and 70th), and the inter-percentile range adjusted for symmetry (IPRAS), for each item being rated. Consensus was defined as items rated “without disagreement”, measured by the IPRAS.

Patient and public involvement.

In this study, members of the public (n = 4) (including two who had participated in trials) were invited to share their thoughts, participate in the ranking process, provided with the outcomes of each round upon completion. They were considered experts due to their lived experience and offered £30 voucher as a compensation for their time.

Delphi participants

Out of 106 international experts approached, and 4 e-mails sent to network mailing lists, forty-nine participants responded to the open round (United Kingdom (n = 37), United States (n = 7), Canada (n = 2), Australia (n = 1), Ireland (n = 1), and Kenya (n = 1)). The panel included a diversity of roles including statisticians (n = 17), trial managers (n = 12), principal investigators (n = 7), funders (n = 4), journal editors (n = 3), member of the public (n = 4), data managers (n = 3), site staff (n = 2), sponsors (n = 2), researchers (n = 2), monitors (n = 2), ethicist (n = 1), clinician (n = 1), CTU manager (n = 1), trial support officer (n = 1), and trial methodologist (n = 1). Many participants had more than one role. See Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0304187.g001

Literature review

We included a total of 78 studies for data analysis (see S1 Fig ), including 6 (8%) reviews, 15 (19%) perspectives or commentaries, 1(1%) interview, 2 (3%) case studies, 2 (3%) surveys and 3 (4%) randomised trials, and 49 (63%) methodologies describing new trial designs. Only 8(10%) studies had explicitly defined or explained what ‘efficiency’ meant in the context of their trials (see S2 Table for details). We categorised discussions of efficiency from the literature into nine key items: 1)scientific efficiency [ 11 , 19 , 20 ], 2)operational efficiency [ 11 , 20 , 21 ], 3)statistical efficiency [ 14 , 22 – 24 ] and 4)economic efficiency [ 15 , 25 ], 5)efficiency in trial designs [ 7 , 8 , 23 , 26 – 45 ], 6)trial conduct [ 11 , 20 , 21 , 46 – 66 ], and other aspects such as 7)improving efficiency using information technologies and mobile apps [ 53 , 67 – 70 ]; 8)involving the public and stakeholders [ 20 , 71 ]; and 9)efficient trial reviews and regulatory approvals [ 28 , 66 , 72 – 74 ]. (see Table 1 for details). These results were included as exemplars in the Delphi open round questionnaire. The detailed description of the literature review has previously been made available [ 75 ] to ensure full transparency and to facilitate open scholarly dialogue.

https://doi.org/10.1371/journal.pone.0304187.t001

When asked to define trial efficiency, some participants referred to definitions from the literature review, while other cited similar definitions tailored to their trial context. When asked about the most efficient/inefficient facets of trial efficiency, the responses resonated closely with the findings from our literature review ( Fig 2 ). Specifically, trial design emerged as the facet most frequently cited as enhancing efficiency, whereas data collection was often highlighted as the element that most impeded efficiency.

The x-axis represents the frequency of responses.

https://doi.org/10.1371/journal.pone.0304187.g002

By incorporating findings from this round, we further refined the nine items identified from the literature review and divided them into two groups: 1) theoretical and abstract constructs: scientific efficiency, operational efficiency, statistical efficiency, and economic efficiency; 2) empirical and fundamental building blocks: trial design (including endpoints selection, statistical analysis plan, protocol development, etc.), trial process (including recruitment and retention, data collection and analysis, trial administration, etc.), superstructure (including regulatory approvals, funding application etc.), infrastructure (including financial and physical resources such as cost, information technologies, routine healthcare data, etc.), and stakeholders. This resulted in a total of nine items for rating in the scoring round (see Table 2 ).

https://doi.org/10.1371/journal.pone.0304187.t002

Scoring round and consensus

Forty participants responded (82%) to the scoring round and there was no disagreement on any items ( Table 2 ). We also conducted sub-analyses by five role groups: (1) funders and sponsors (n = 6); (2) statisticians (n = 13); (3) trial managers (n = 10); (4) principal investigators (n = 6); and (5) PPIs (n = 3). Group membership was not mutually exclusive. Stratified results showed widespread agreement that the items were appropriate, with the exception of one of the building blocks–superstructure. The funders and sponsors group disagreed this item was appropriate ( S3 Table ). As a result, no new items were added but we slightly modified the explanation of each proposed item, in line with free-text comments made by the participants.

Theoretical constructs of trial efficiency: Revised definitions incorporating Delphi comments

Scientific efficiency..

Some participants were confused by the provided definition ( Box 1 . quote 1); while some suggested expanding the definition with the inclusion of feasibility and implementation ( Box 1 . quotes 2–3). As such, we refined the definition as the balance of methodological rigour, relevance of the research question, and feasibility of trial design. It prioritises effective use of resources, including data, to minimise research waste, considers the alignment of design and statistical strategies, and underscores the importance of the study’s practical impact on stakeholders and delivering value to end-users.

Operational efficiency.

Some comments suggested the definition should be expanded to consider operation feasibility, bureaucracy, and ongoing evaluation ( Box 1 . quotes 4–6). Therefore, we modified operational efficiency as the optimal management, organisation, execution, and continuous evaluation of trial processes and procedures. It emphasises operational feasibility (such as ensuring there are enough workforce, managing delays, and working effectively with third-party providers), reducing unnecessary bureaucracy and duplication, and continuously assessing the trial for potential improvements.

Statistical efficiency.

The initial definition ( Table 1 ) was expanded based on the participants’ comments (Box1. quotes 7–8), as the application of design and analytical methods that result in more accurate estimates of treatment effects or other parameters of interest. This includes considerations of minimising the amount of data to be collected, accounting for missing data, and managing sources of bias or confounding; its focus is specifically on maximising the accuracy and reliability of results given the data collected.

Economic efficiency.

We increased the clarity of the initial definition according to scoring round feedback ( Box 1 .quotes 9–10): the optimal use of resources in the trial design, implementation and analysis, to ensure immediate and long-term cost-effectiveness of the trial. This focus on value ensures that resources are utilised to their fullest extent without compromising the quality of the research. It emphasises on the cost-effectiveness of conducting the trial.

Box 1. Scoring round exemplar free-text comments related to the construct definitions

Scientific efficiency.

• Quote 1 : “Not sure rigour equates to efficiency” (Participant n. 17, principal trial investigator)
• Quote 2 : “Feasibility of trial design needs to be included here. You could have the perfect trial design but no participants or high withdrawals and lack of site engagement.” (Participant n.2, trial manager)
• Quote 3 : “This may also need to include how important the findings will be to service users and the public and whether there are ways they are expected to be implemented in practice.” (Participant n.28, trial support officer)

Operational efficiency

• Quote 4 : “I’d make particular focus on the bureaucracy ‐ endless paperwork.” (Participant n.3, funder)
• Quote 5 : "Feasibility of operational efficiency. You may have participants and engaged sites but you need operational feasibility to align." (Participant n.2, trial manager)
• Quote 6 : “Would like to see reference to the ongoing assessment of a trial in the descriptor.” (Participant n.39, trial manager)

Statistical efficiency

• Quote 7 : “and accounting for missing data, and sources of bias or confounding” (Participant n.19, principal trial investigator)
• Quote 8 : “Also needs to encompass other aspects of analysis, e.g., health economics.” (Participant n.14, statistician)

Economic efficiency

• Quote 9 : “Allowing for the concept of data sharing beyond the life of the study” (Participant n.37, sponsor)
• Quote 10 : “Need to be clear that this is (I presume) related to the costs of delivering the trial and not the cost of the intervention (i.e. health economic analysis).” (Participant n.26, statistician)

Essential building blocks comprising an efficient trial

Overall, there was a strong consensus on the building blocks; the free-text comments did not suggest significant alterations, but recommended adding some details within each building block. Trial design concerns the planning and organisation of a trial, which may include the trial methodologies, research questions, sample size, interventions, control group, endpoints and outcomes; document development such as funding application; as well as planning feasibility and pilot studies. The trial process involves the setup, execution, and closeout phases of a trial (see S2 Fig for details). Stakeholders are the critical human factor, they are individuals or groups with an interest or concern in the design, execution, and outcomes of a trial. They could be trial participants (e.g. patients, practitioners, health system leaders, public health organisations, etc.), trialists (e.g. investigators, researchers, trial managers, statisticians, etc), funders, sponsors, trial sites and their staff, regulatory authorities, healthcare and clinical practitioners, the scientific community (researchers, academics, and clinicians interested in the trial’s outcomes and its implications for future research) and the general public (the broader population who may ultimately benefit from the knowledge generated by the clinical trial). Infrastructure is the underlying framework, systems, and resources required to design, implement, manage, and analyse a trial, such as resources (human, financial, physical), information systems and technologies, and healthcare data. Superstructure serves as the overarching structure of a trial, including laws, policy, and governance.

With these, we developed a Trial Efficiency Pentagon ( Fig 3 ) to place the five building blocks and to illustrate the multiple connections among them ‐ improvements in one block may potentially lead to trade-offs in one or more other blocks.

https://doi.org/10.1371/journal.pone.0304187.g003

The final conceptual framework for defining trial efficiency

Fig 4 represents the finalised framework. The term trial efficiency is complex and multifaceted, encompassing four conceptual constructs with five essential building blocks.

The outer blue circle outlines theoretical constructs of trial efficiency: Scientific Efficiency, Statistical Efficiency, Operational Efficiency and Economic Efficiency. At its core, the inner pentagon outlines the empirical building blocks: Superstructure, Stakeholders, Infrastructure, Trial Process, and Trial Design. The cyclical arrows indicate the necessity for a balanced consideration of each building block within each construct to optimise trial efficiency.

https://doi.org/10.1371/journal.pone.0304187.g004

Main findings

Consensus was achieved on the four constructs that together define trial efficiency: scientific efficiency, operational efficiency, statistical efficiency and economic efficiency; and the five essential building blocks for considering an efficient trial: trial design, trial process, infrastructure, superstructure, and stakeholder.

The conceptual constructs, empirical building blocks, and interrelationships

Overall there was no disagreement over the constructs that conceptually define trial efficiency. However, some concerns were raised regarding potential overlaps, between scientific efficiency and statistical efficiency, and between operational efficiency and economic efficiency ( S4 Table ). These four constructs share some common elements. However, they are conceptually distinct and each construct brings unique aspects to the concept of trial efficiency. Scientific efficiency, for instance, focuses primarily on the methodological rigour [ 77 ] and feasibility of trial design, while statistical efficiency is concerned with achieving the most accurate results possible with the smallest amount of data collected [ 78 ]. The overlap lies in the fact that both aim to optimize the quality and validity of the trial’s findings, yet their distinct focus underlines their separate roles within the overarching construct of trial efficiency. Similarly, while operational and economic efficiency both aim to make the best use of resources [ 11 ], they do so in different ways and in different contexts. Operational efficiency is about the effective management and organization of trial processes and procedures [ 11 , 13 ], while economic efficiency involves optimizing resource use in relation to the cost of delivering the trial. By maintaining these conceptually distinct constructs, we were able to capture the broad spectrum of abstract factors that define trial efficiency, thus offering a nuanced theoretical framework for its comprehension.

The proposed building blocks create a foundation for the formulation of an efficient trial. In the Delphi scoring round, there was strong consensus regarding the significance of these building blocks, with an average median score of 8.4 on a 1–9 scale. However, some participants perceived hierarchy among the building blocks, suggesting that some (e.g., trial design and process) hold more importance than others. This was reflected in the literature review and responses in the Delphi open round, where certain building blocks ‐ such as trial design ‐ were more frequently discussed as critical determinants of trial efficiency. Despite these observations, we propose that all five building blocks have equal importance and they mutually contribute to the overall efficiency of the trial. These foundational elements are also interconnected, for instance, even the most rigorous and feasible trial design is contingent upon the availability of suitable infrastructure support and requires inputs from stakeholders. Therefore, we advocate for a balanced view where no single building block takes precedence in the trial efficiency pentagon.

There is a layered connection between the constructs and the building blocks: the constructs were conceptualised to provide a broad, overarching view of efficiency within healthcare trials. In contrast, the building blocks were identified as the essential, practical components that operationalise efficiency in real-world settings. In addition to this relationship, we suggest that for a comprehensive understanding, each efficiency construct takes into account all five building blocks. For instance, while it may seem apparent that scientific efficiency is closely linked with trial design, focusing on how the study is conceptualised to ensure methodological soundness; it also intersects with stakeholder involvement, where patient and public engagement can improve the trial design and thus the trial outcomes’ relevance and applicability.

Implications

According to the results from the literature review, few studies explicitly defined efficiency in the context of trials and no effort has been made to develop a unified and agreed definition for trial efficiency. Linguistically, ‘efficiency’ is defined as “the production of the desired effects or results with minimum waste of time, effort, or skill” [ 79 ]. This definition shares similarities with those from the literature ( S2 Table ), wherein the outstanding characteristic corresponds to the balance between the inputs (e.g. resources) and the outputs (e.g. the objectives of the trial). Nevertheless, these interpretations are often narrowly tailored. In this study we hoped to offer a holistic view that captures the nuances and complex aspects of trial efficiency and which may benefit policymakers, funders, and researchers in making informed decisions, leading to improved trial implementation and patient care. Enhancing efficiency was emphasised in the UK Department of Health and Social Care’s 2022–2025 strategic plan for clinical research [ 80 ]. As of the drafting of this paper, the U.S. Food and Drug Administration is announcing the updated recommendations for good clinical practices advocating for greater efficiency in trials by modernising both design and conduct [ 81 ]. Therefore, it is evident that our study is timely, positioning the urgency of comprehensively understanding trial efficiency.

Strengths and limitations

Drawing on both literature review and expert opinion, our study followed a rigorous approach to develop a conceptual framework of trial efficiency. We included a wide range of experts in trial communities including members of the public, enhancing the comprehensiveness and richness of our study. Nevertheless, nine participants did not respond to the scoring round, which could have introduced potential biases in reaching a consensus or perhaps missed subtle distinctions regarding the significance of certain trial elements. However, given the diverse range of participants who did engage, coupled with the triangulation with existing literature, this non-response is not expected to significantly impact the overall validity and comprehensiveness of our Delphi findings.

While we have sought to delineate each construct and building block distinctly, we acknowledge the potential for different interpretations of qualitative data. The interplay between the identified themes is likely to be more intricate, reflecting the complex nature of trial efficiency. Future research could delve deeper into this interplay to unravel the connections.

The ’trial efficiency pentagon’, emerging as a novel concept from this study, is a promising tool for assessing trial efficiency (proactively and retrospectively). For example, it could be developed to support group discussions and/or calibrated as an evaluation instrument to measure the efficiency of a trial. However, it is limited by lacking robust theoretical foundation. To elucidate, while we’ve pieced together insights and perspectives to shape the pentagon, we have not rooted it in any established theory or conceptual model. This could mean that certain fundamental aspects of trial efficiency might be overlooked or not holistically represented. In the future, we aspire to hone the pentagon into an evidence-based, theory-informed tool and we welcome insights from our readers and remain open to potential collaborations to its further development.

This is the first attempt to dissect the concept of trial efficiency into theoretical constructs. In this pursuit of understanding, we are not only unravelling the complexities of trial efficiency but also laying the groundwork for evaluating the efficiency of an individual trial or a trial system in the future.

Supporting information

S1 fig. prisma flowchart..

https://doi.org/10.1371/journal.pone.0304187.s001

S2 Fig. Trial process in general.

https://doi.org/10.1371/journal.pone.0304187.s002

S1 Table. Literature review inclusion and exclusion criteria.

https://doi.org/10.1371/journal.pone.0304187.s003

S2 Table. Efficiency definitions/explanations in the literature.

https://doi.org/10.1371/journal.pone.0304187.s004

S3 Table. Scoring round stratified results.

https://doi.org/10.1371/journal.pone.0304187.s005

S4 Table. Scoring round exemplar quotes related to potential overlaps among the four constructs.

https://doi.org/10.1371/journal.pone.0304187.s006

S1 File. Open round questionnaire.

https://doi.org/10.1371/journal.pone.0304187.s007

Acknowledgments

We thank Prof. Shaun Treweek for his insightful discussion on trial efficiency, which has largely inspired this work. We thank Ann Thomson, Senior Trial Manager at Queen Mary University of London’s Pragmatic Clinical Trials Unit, for her valuable discussions and insights into the trial process. Our thanks also go to the Health Research Board ‐ Trials Methodology Research Network for their assistance in promoting our Delphi study through their email newsletter. We acknowledge the support of the UKCRC Registered CTU Network. The views expressed are those of the author(s) and not of the UKCRC or its members. We are immensely thankful to all participants of the Delphi study rounds for their invaluable contributions and willingness to share their expertise. We have received consent to acknowledge the following participants by name (with no particular order): Monica Taljaard, Lelia Duley, Sarah Markham, Deb Smith, Catey Bunce, Stephen Brealey, Steff Lewis, Laura Miller, Jacqueline French, Fiona Hogarth, Gail Holland, Nikki Totton, Nick Kisengese, Joanne Haviland, Matthew Burns, Richard Hooper, Claire Ayling, Catherine Arundel, Ines Rombach, Seonaidh Cotton, Paula Kareclas. Lastly, we appreciate the reviewer’s comments, which have been instrumental in enhancing the development of the conceptual framework.

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Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework

• Original Research Article
• Open access
• Published: 17 May 2024

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• Askal Ayalew Ali   ORCID: orcid.org/0000-0002-0535-6300 1 ,
• Amit Kulkarni 2 ,
• Sandipan Bhattacharjee 3 &
• Vakaramoko Diaby 2  

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Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector.

We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application.

The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD.

The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

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1 Introduction

The link between economy-wide prosperity and health is increasingly recognized [ 1 ]. Compelling evidence from studies [ 2 , 3 ] and the World Health Organization (WHO) report [ 4 ] highlight the reciprocal relationship between economic development and health outcomes. Health plays a crucial role in human capital, contributing to economic growth and development [ 5 ]. A healthy population is more productive, with higher labor force participation and income generation. Consequently, this leads to increased tax revenues and greater government spending on social programs [ 6 ]. Thus, healthcare interventions are of critical importance in maintaining a healthy population, promoting economic growth, and enhancing societal well-being.

Health technology assessments (HTAs) are of paramount importance in determining the value of healthcare interventions. They are used to assess the benefits and costs of new technologies, including pharmaceuticals, medical devices, and procedures, with the aim of informing decisions about their use and reimbursement [ 7 ]. Healthcare interventions may have impacts beyond direct health outcomes, such as changes in productivity and social welfare. As a result, decision makers using traditional HTA approaches may overlook significant benefits or harms associated with healthcare interventions that extend beyond the healthcare system itself [ 8 ]. For instance, a treatment that reduces disability and improves productivity among patients can generate economic gains by enhancing workforce participation and reducing long-term disability costs. Neglecting such economic impacts in HTA evaluations may undervalue the true value of interventions and hinder informed decision making [ 9 ]. Additionally, healthcare interventions have profound societal impacts beyond the individual patient. For example, interventions that improve population health can lead to reduced healthcare inequalities, increased social cohesion, and improved overall well-being [ 10 ]. These broader societal implications are often overlooked in traditional HTA frameworks, limiting the ability to assess the true societal value of healthcare interventions. Yet, current HTA frameworks fail to fully capture the impacts of healthcare interventions on broader society beyond the healthcare sector [ 11 , 12 , 13 ]. Furthermore, the limited scope of HTAs may result in an unequal distribution of benefits and harms across different population groups, leading to concerns about equity [ 14 , 15 ]. This gap in the literature points to the critical need for novel frameworks that consider the broader economic and societal impacts of healthcare interventions beyond the health sector. Such frameworks would need to capture the interactions between healthcare interventions and the fabric of the economy such that economic and societal ripple effects are fully estimated. By doing so, decision makers can make more informed choices that align with societal priorities and promote equitable access to effective healthcare interventions.

In this manuscript, we document examples in which available evidence stemming from current HTA does not traditionally account for the impacts of healthcare interventions on broader society, beyond the healthcare sector. We then present a novel conceptual framework for the value assessment of healthcare interventions. The framework accounts for direct health outcomes, as well as ripple effects beyond the healthcare sector.

2 Current Health Technology Assessment (HTA) and its Limitations

HTAs play an essential role in assessing the value through quantifying their impacts on the healthcare and non-healthcare sectors of our society. However, it is crucial to recognize limitations of traditional HTA approaches in capturing the full spectrum of societal effects that extend beyond the immediate healthcare context. While current HTA approaches excel in assessing clinical effectiveness and cost effectiveness within the healthcare system, they often overlook the significant economic and productivity impacts on the broader economy. The introduction of new medical technologies can stimulate job creation, enhance productivity, and foster economic growth in related sectors, yet these broader economic impacts frequently escape the traditional HTA analysis framework [ 16 ]. This oversight underscores a critical gap in evaluating the true value of healthcare interventions.

Moreover, the traditional focus of HTAs on clinical outcomes and economic factors tends to sideline the profound social and ethical implications of health technologies. Interventions such as genetic testing and gene therapies introduce complex ethical dilemmas, including issues related to privacy, discrimination, and equity [ 17 ]. The broader social repercussions of these technologies, such as their influence on social inequalities and cultural norms, are often insufficiently considered in HTAs that prioritize clinical and economic metrics above all.

Environmental sustainability is another aspect frequently neglected in HTAs. The lifecycle of medical devices and pharmaceuticals—from production to disposal—poses significant environmental risks, including pollution and resource depletion [ 18 ]. A comprehensive evaluation of healthcare interventions demands consideration of their environmental footprint, encompassing carbon emissions, waste production, and overall ecological impact, factors traditionally excluded from HTA methodologies [ 19 ].

The reliance of HTAs on short-term, clinical trial-based evidence further limits their ability to grasp the long-term and systemic effects of healthcare interventions. The broader implications of new technologies, such as their impact on healthcare delivery, patient/provider dynamics, and the healthcare system at large, may only become apparent over time and are often overlooked in the HTA process [ 20 ]. This shortcoming highlights the need for an expanded evaluation framework that can capture a larger range of impacts.

Lastly, the reach of healthcare interventions extends well beyond the healthcare sector, touching upon areas such as education, transportation, and urban planning. Therefore, assessing the comprehensive societal impacts of healthcare innovations calls for intersectoral collaboration, a crucial element not consistently integrated into traditional HTA practices [ 21 ]. This gap underscores the necessity of adopting a more holistic approach to HTA, one that fully appreciates the multifaceted impacts of healthcare interventions.

In summary, while HTAs are invaluable for assessing healthcare interventions, their traditional methodologies fall short in several key areas. Expanding the scope of current HTAs to encompass economic, social, ethical, environmental, and systemic factors will provide a deeper understanding of healthcare innovations’ true value. For example, a more nuanced and comprehensive HTA will allow us to (1) better allocate the novel health technology to those most in need; and (2) provide incentives for continual innovation ensuring dynamic efficiency. Such an expanded approach would not only enhance the relevance of HTAs but also support more informed and equitable healthcare decision making.

3 Conceptual Framework

This section presents a multipronged approach to the assessment of technology that builds on (1) conventional cost effectiveness and inclusive of patient-centeredness and equity (microeconomic and qualitative approaches combined), followed by (2) a macroeconomic analysis of the intervention’s impacts, and (3) a voting scheme consisting of a combination of the outputs of both (1) and (2) to create decision rules for the adoption of the intervention like the four quadrants of the cost-effectiveness plane (Fig.  1 ).

Proposed conceptual framework

3.1 A Conventional Cost-Effectiveness Analysis Augmented with Patient Centricity and Equity: Distributional Cost-Effectiveness Analysis (DCEA)

Distributional cost-effectiveness analysis (DCEA) enhances traditional cost-effectiveness analysis (CEA) by incorporating equity considerations, assessing how healthcare interventions impact different population subgroups and their distributional effects [ 22 ]. DCEA aims to uncover the effects of interventions on health inequalities, determining whether benefits are equitably distributed or exacerbate disparities. Implementing DCEA involves several steps, which are explained in detail elsewhere [ 23 ]. Briefly, we present steps conducive to a successful implementation.

Step 1: Defining the relevant groups of interest: This step starts with identifying relevant population groups by geography, age, or socioeconomic status [ 24 ], ensuring data availability for each. The disease’s prevalence or incidence in each group is then measured, followed by evaluating the intervention’s effectiveness in improving health outcomes, measured in quality-adjusted life-expectancy (QALE) for each subgroup.

Step 2: Establishing the baseline QALE: Establishing the baseline QALE for each socioeconomic group is the cornerstone of DCEA. This step involves collecting data on life expectancy and adjusting it for quality of life, typically derived from health surveys, life tables, and disease registries. The quality adjustment reflects the utility value of different health states, which can be gathered from the literature or through population surveys. Researchers must ensure the data are representative of the different predefined subgroups in Step 1 to accurately reflect the baseline health disparities. This step sets the stage for the analysis by providing a snapshot of the health status quo before any intervention (pre-intervention).

Step 3: Determining the average QALE: Determining the average QALE across groups is a critical step that sets the stage for identifying and quantifying health inequalities. The average QALE is calculated across all socioeconomic groups’ prespecified subgroups in Step 1 to establish the reference point against which disparities will be measured. This average serves as a benchmark to identify the extent of baseline inequalities in health outcomes.

Step 4: Applying the inequality aversion parameter (α): The inequality aversion parameter (α) is applied using an exponential function to the difference between individual QALEs and the average QALE [ 22 ]. This quantifies societal discomfort with inequality, where a higher exponential value indicates greater aversion, particularly for disadvantaged groups. This step requires an understanding of how to model preferences and may involve sensitivity analyses to explore how different values of α affect the results.

Step 5: Introducing the interventions: This step involves the application of health interventions to the model to evaluate their impact on QALE. For each socioeconomic group, we reassess QALE post-intervention. This requires detailed intervention data on effectiveness and utilization across different groups, often sourced from clinical trials, observational studies, and healthcare databases.

Step 6: Reassessing inequality with the Kolm Index: Post-intervention, inequality is reassessed using the Kolm Index, which accounts for absolute differences in health outcomes across groups. Calculating the Kolm Index post-intervention involves applying the α parameter to the differences in QALE post-intervention and the average QALE [ 22 ]. This index is particularly responsive to changes at the lower end of the health distribution, thus highlighting the intervention’s impact on the most disadvantaged groups.

Step 7: Computing the equally distributed equivalent (EDE) post-intervention: Finally, the EDE QALE post-intervention is calculated, which represents the average QALE that would result if the health gains observed were distributed equally across all socioeconomic groups. This step may utilize mathematical programming or simulation techniques to redistribute health gains and compute the EDE. It is a crucial step as it synthesizes the equity considerations into a single measure, allowing for a comparison of the equity impact of different health interventions.

Step 8: Uncertainty analysis: DCEA extends traditional CEA by incorporating differential effects of health interventions across socioeconomic strata. This methodology enables the quantification of an intervention’s influence on overall population health while simultaneously capturing its impact on health equity among distinct population groups. Central to DCEA is the assessment of uncertainty, which pertains to both the potential health benefits of an intervention and its effects on health inequality [ 33 ]. Addressing decision uncertainty is critical, as it informs the confidence in policy recommendations derived from DCEA outcomes. Two principal analytical methods are employed in this assessment: value of information (VOI) analysis and analysis of covariance (ANCOVA). VOI analysis quantifies the expected benefit of acquiring additional information, thus elucidating the value derived from mitigating uncertainty. This method aids in pinpointing where further research could yield significant impacts on policy direction, based on the existing evidence base. It provides a systematic approach to evaluating whether the potential reduction in uncertainty justifies the investment in additional data collection or research, particularly when an intervention shows promise but its distributional effects remain uncertain. ANCOVA complements VOI analysis by statistically parsing the factors contributing to outcome variability in a DCEA. Through this analysis, researchers can investigate how different covariates—such as baseline health conditions, adherence to treatment protocols, and differential effectiveness across demographic profiles—affect the cost effectiveness and equity dimensions of an intervention. This methodological approach is instrumental in identifying the sources of uncertainty and guiding subsequent research to areas that promise the greatest impact on policy decisions. The incorporation of uncertainty analysis into DCEA enhances the ability to inform HTA decisions that are both data-driven and ethically informed, ensuring that efforts to improve health outcomes are aligned with the goal of reducing disparities across socioeconomic groups.

Each parameter and step in this DCEA approach is critical to providing insight into both the effectiveness and equity of health interventions. The QALE offers a measure of the health benefits of interventions, while α and the Kolm Index bring the equity dimension into focus. Together, they facilitate a comprehensive assessment that informs which interventions can deliver the greatest health benefits across all societal groups equitably. As such, DCEA extends the focus of HTA to include patient-centered outcomes and equity considerations, addressing potential disparities in healthcare access and outcomes. However, it stops short of capturing broader societal impacts of health technologies. Thus, we recommend augmenting DCEA with macroeconomic analysis to explore the wider economic effects of healthcare interventions, providing a more complete assessment of their societal value.

3.2 Extended HTA Analysis with Input-Output Modeling

3.2.1 overview of the input-output model.

The input-output model can be effectively utilized to assess the wide-ranging economic effects of healthcare interventions. The model, conceived by economist Wassily Leontief [ 25 ], is a practical tool for mapping out how different sectors of the economy interact with one another, showing us how the activities of the healthcare sector can have ripple effects throughout the economy.

Extended HTA analysis, augmented with input-output modeling, broadens the scope of traditional HTA by integrating techniques that quantify the wider economic impacts of healthcare interventions. This enhanced approach provides a thorough evaluation of secondary effects, including shifts in productivity, innovation, and social welfare. Originating from the work of economist Wassily Leontief [ 25 ], the input-output model serves as a valuable instrument for charting the intersectoral dynamics of the economy, highlighting the far-reaching impact that healthcare sector activities have beyond their direct environment.

At its core, the input-output model employs a matrix (input-output table) to manifest the transactions between sectors within the economy, painting a detailed picture of how expenditures and consumptions traverse through various industries. The input-output table is instrumental in visualizing the intersectoral flow of economic value, quantifying how healthcare investments can propagate through and impact the economy at large.

By embracing the linear relationships intrinsic to this model, we can project how changes within the healthcare sector influence the production functions of interconnected sectors. Although the model traditionally operates on the premise of fixed coefficients and an inexhaustible supply capability, assumptions that offer a degree of simplification, the analysis acknowledges and adjusts for these parameters to ensure nuanced and realistic estimations.

Furthermore, while the input-output model often considers a closed economy, the approach can expand this horizon to encapsulate the dynamism of global trade, thereby refining the understanding of healthcare interventions’ international repercussions, as needed.

This approach not only underscores the direct impact of healthcare interventions but also illuminates their extended influence on sectors seemingly remote from healthcare. It equips us with a quantitative foundation to argue for a more comprehensive appreciation of the healthcare sector’s contributions, advocating for policies that recognize and reward the full spectrum of impacts that healthcare interventions yield across the entire economy. The following subsections outline the steps necessary for the successful implementation of this approach.

Step 1: Choice of aggregation level: In an input-output model, aggregation analysis refers to the grouping of economic sectors and activities for analysis. This allows for a more manageable analysis, as many sectors can be consolidated into broader categories, rather than analyzing each sector individually. The aggregation level of the analysis depends on the research question being addressed, the availability of input-output tables and the data on the final demand, which is the spending component of the gross domestic product (GDP). Typically, input-output tables are created with data related to specific economic areas, which may be the national economy and also an economic region. One can construct a symmetric industry-by-industry input-output table (SIOT) from the ‘Make and Use’ tables in the ‘After Redefinition Tables’ of the Bureau of Economic Analysis (BEA) [ 26 ]. The latter reveals how products flow in the economy, from being sold (rows) to being purchased (columns). Each industry is identified using the North American Industry Classification System (NAICS) [ 27 ]. Such industries, which are directly relevant to the analysis, are identified based on their contribution to the economic activity under consideration. This may include industries involved in producing goods or services that impact the healthcare sector and disease state for which the intervention is devised.

Step 2: Analyzing the characteristics of a SIOT: The SIOT is divided into four sections, each serving a unique purpose (Online Resource 1 eTable 1). The top left section shows the exchange of materials and services between industries, known as intermediate inputs. It helps us understand how industries rely on one another. The bottom left section focuses on the value-added components within each industry. It reveals the portion of the final price generated within each industry, such as wages, profits, and taxes. The top right section represents the final demand. It captures the total amount of products and services consumed by households, businesses, and the government. The lower right section of the table is left empty as it is not used in this analysis for relevance purposes. In fact, the model is simplified to focus on the key sectors and flows of interest, leaving out parts of the matrix that do not contribute to the analysis at hand (Online Resurce 1 eTable 1).

Step 3: Calculating the Leontief inverse: Upon establishing the SIOT, we can calculate technical coefficients. These coefficients reflect the proportions of inputs utilized by various industries to generate their outputs, serving as a fundamental aspect of input-output analysis [ 28 ]. Unlike variable coefficients in other models, these coefficients are assumed to be constant and are organized into what is known as the direct coefficient matrix. This matrix forms the basis for analyzing the input-output relationships within the economy, following a specific production function termed the Leontief production function [ 29 ].

The Leontief production function is distinctive because it assumes fixed input ratios, simplifying the analysis of economic interdependencies. This function is operationalized by subtracting the technical coefficient matrix from an identity matrix, often referred to as the unity matrix. The identity matrix is characterized by ones on its main diagonal and zeros elsewhere, establishing a standard reference for matrix operations.

To assess the impact of a change or shock in a specific industry, economists employ multipliers. These multipliers are derived from the inverse of the matrix formed by the Leontief production function, known as the Leontief inverse. They are crucial in measuring the ripple effects of a change in one industry on the broader economy. By using these multipliers in specific equations, we can calculate the overall economic impact of various shocks or changes.

For instance, consider a scenario where there is an increase in demand in the automotive industry. The multipliers can help quantify how this demand increase affects not only the automotive supply chain but also other unrelated sectors and the economy at large. This ability to model economic interconnections is particularly valuable in scenarios and policy simulations [ 30 ].

Step 4: Applying the input-output model using simulation scenarios: Through input-output analysis, policymakers and analysts can simulate the effects of various interventions—such as drugs, adjustments in government policies, investment levels, or shifts in demand/supply patterns—to understand the net broader impacts on the economy. As a first step, the model simulates the economy’s baseline estimate using multiplier coefficients that connect economic variables. This baseline scenario represents the current economic situation without considering the intervention’s economic consequences. In the second step, the model creates an alternative scenario by introducing exogenous changes in the economic variables for the US economy over a specific period to explicitly reflect the impact of the intervention. By comparing the outputs of the control scenario with those of the simulation accounting for the exogenous changes, we can quantify the net economic impact. There could be more than one alternative scenario that reflects several interventions or policies that are envisioned to be assessed or analyzed.

Step 5: Uncertainty analysis: Given the assumptions and potential for incomplete data, uncertainty analysis is crucial for interpreting the input-output model results. It begins with identifying sources of uncertainty, such as the estimation of technical coefficients. Sensitivity analysis follows, pinpointing impactful variables. This consists of quantifying the identified sources of uncertainty using appropriate statistical methods. Moreover, VOI analysis can be utilized to gauge the potential advantages linked with diminishing parametric uncertainty [ 31 ].

The literature suggests several approaches to quantifying uncertainty in input-output modeling [ 32 ]. First, the deterministic error analysis sets boundaries for variables, exemplified by establishing limits on the costs of raw materials and the output of pharmaceutical products. This is complicated by the Leontief inversion, a non-linear mathematical process translating inputs into outputs. The econometric estimation of input-output coefficients then uses statistical methods to understand relationships within the economy. For instance, it might analyze how demand in the chemical industry affects pharmaceutical production, based on data from different business sectors. As for the error transmission method, it focuses on neutralizing random errors to improve result accuracy. This is illustrated by a pharmaceutical company predicting a 10% reduction in hospital admissions with a new drug. If the actual reduction is lower, the discrepancy, potentially caused by random factors like patient adherence, is analyzed to refine the drug’s economic impact assessment. Finally, the full probability density function approach aggregates data from various sources to create a more accurate overall picture, while the extended Monte Carlo simulations and Bayesian/Entropy methods are used for advanced equilibrium analysis and data treatment, enhancing model reliability. Additionally, scenario analysis can be conducted to explore different outcomes under varying conditions. The input-output model is iteratively refined with new data, with methods that are transparently documented, ensuring reproducibility.

3.3 Key Assumptions to Consider When Using DCEA and Input-Output

When employing DCEA, it is essential to underpin the analysis with several critical assumptions. These assumptions frame the interpretability and generalizability of the results. It is assumed that the QALYs gained from an intervention are comparable across different socioeconomic groups, which may not always reflect the complexity of real-world effectiveness [ 34 ]. This assumption of uniform QALYs valuation requires careful consideration, as the perception of health gains could be influenced by factors unique to each group [ 22 ]. Another key assumption is that the intervention’s cost effectiveness derived from trial or modeled data can be extrapolated to different population subgroups. This extrapolation assumes that the intervention’s relative effectiveness and resource utilization are constant, which may not hold true across diverse socioeconomic settings. Additionally, DCEA presupposes that the societal perspective for the value of reducing inequality, as expressed by the inequality aversion parameter (α), is accurately captured and constant. This parameter’s selection is subject to ethical considerations and may significantly influence the outcome of the analysis [ 35 ]. It is also crucial to acknowledge that the methods used to quantify health inequalities, such as the Kolm Index or the Concentration Index, are based on assumptions about the social welfare function and the distribution of health states within a population [ 36 ]. Lastly, when interpreting the results of a DCEA, it is typically assumed that policymakers will act rationally upon the evidence presented, incorporating both cost effectiveness and distributional impacts into their decision-making processes [ 37 ].

In employing an IO model for economic analysis, we must carefully consider the underlying assumptions that govern its framework. The model is predicated on the notion of linear relationships between inputs and outputs across different sectors. It assumes a proportional scale of production, where inputs and outputs increase or decrease in unison. While this linear perspective facilitates a straightforward computational approach, it may not encapsulate the complexities of real-world production where returns to scale can vary dramatically.

Furthermore, the input-output model is built on the assumption of fixed coefficients. These coefficients, which dictate the input needs per unit of output, are considered static. However, this assumption does not hold up well under the dynamic conditions of a real economy where technological advancements and shifts in relative prices can lead to substitutions among inputs, thus altering these coefficients over time.

Another critical assumption is the absence of supply constraints within the model. It posits that sectors have the unfettered capability to meet any level of demand. This ignores the finite nature of resources and the production ceilings imposed by factors such as limited natural resources or production capacity, which are very real challenges that industries face.

Lastly, the model, in its more rudimentary form, operates on the assumption of a closed economy. This means it does not account for the intricacies of international trade, which are integral to most modern economies. Although more sophisticated iterations of the model do consider trade, this simplification can lead to significant inaccuracies in economic analysis when using the basic version of the model.

These foundational assumptions are essential to the input-output model’s operation, yet they also delineate its limitations. Analysts must recognize and adjust for these limitations when applying the model to real-world economic situations, to ensure that the insights derived are both relevant and robust.

3.4 Decision Rules Through Voting Scheme

A voting scheme is established to guide the adoption of interventions. It delineates four distinct quadrants, each representing different combinations of net health benefits and net broader impacts (Fig.  2 ). Quadrant I embodies the epitome of an ideal choice, where interventions not only yield positive augmented net health benefits but also generate extensive net broader impacts. These interventions, characterized by their ability to provide the highest value for money, are actively sought after by organizations and individuals striving to maximize the impact of their allocated resources. Transitioning to quadrant II, we encounter interventions that manifest positive augmented net health benefits but are accompanied by a negative net broader impact. The pursuit of options in this quadrant is warranted when the augmented net health benefits outweigh the negative broader impacts, thereby justifying the investment in the intervention. While acknowledging the existence of some unfavorable consequences, the overall positive health outcomes make this quadrant a viable and prudent choice. Conversely, quadrant III encompasses interventions that exhibit both a negative augmented net health benefit and a negative net broader impact. As such, these interventions are deemed inadequate choices, as they engender detrimental outcomes. To ensure optimal decision making, options within this quadrant should be unequivocally rejected due to their deleterious effects. Lastly, quadrant IV comprises interventions with a negative augmented net health benefit but a positive net broader impact. In certain circumstances, organizations or individuals may opt to pursue options within this quadrant when the positive societal impacts outweigh the negative augmented net health benefits. Consequently, the investment in such interventions can be deemed justified, despite their inability to directly enhance health outcomes.

Health technology assessment decision quadrants

The voting scheme is designed to integrate multiple stakeholders’ perspectives, including patients, healthcare providers, policymakers, researchers, advocacy groups, payors, and community representatives, ensuring a balanced and inclusive approach to decision making. This integration occurs through a structured voting process where each stakeholder group is given a platform to express their priorities and concerns regarding the interventions under consideration.

Patients provide insights based on their personal experiences and preferences, highlighting the direct impact of interventions on their health and well-being. Healthcare providers, such as doctors and nurses, offer a clinical perspective, evaluating the interventions’ efficacy, safety, and feasibility within the healthcare system. Policymakers consider the broader societal impacts, including economic, ethical, and social implications, ensuring that interventions align with public health goals and resource allocation principles. Stakeholders cast their votes for or against the adoption of interventions, with the results aggregated to identify the options with the broadest impact.

To reconcile differences and reach a consensus, the scheme may employ weighting systems, negotiation rounds, or expert panels to further deliberate on contentious issues. This multifaceted approach facilitates a democratic and transparent decision making process, where the diverse values and priorities of the community are reflected in the final choices [ 38 ].

A detailed step-by-step explanation of the proposed voting scheme, integrating elements of decision conferencing [ 38 ] for a compelling and inclusive approach to decision making, is as follows:

Step 1: Stakeholder identification and engagement: The first step involves identifying key stakeholders, including patients, healthcare providers, policymakers, researchers, industry representatives, advocacy groups, payors, and community leaders. Each stakeholder group brings a unique perspective and expertise to the decision-making process.

Step 2: Preparation and information sharing: Prior to voting, stakeholders are provided with comprehensive information about the healthcare interventions under consideration. This includes evidence-based data on efficacy, safety, cost effectiveness, and potential societal impacts. Decision conferencing techniques are employed to facilitate constructive dialogue and knowledge exchange among stakeholders.

Step 3: Voting process: Stakeholders are presented with a set of options for each healthcare intervention, ranging from adoption to rejection. They cast their votes based on their assessment of the options, taking into account their respective interests and expertise. Votes are submitted anonymously to encourage candid and unbiased decision making.

Step 4: Aggregation and analysis of results: Once all votes are collected, they are aggregated and analyzed to determine the level of support for each option. This involves tallying the votes and identifying patterns or trends across stakeholder groups. Decision conferencing tools such as visualizations and simulations may be used to facilitate data interpretation.

Step 5: Reconciliation and consensus building: In cases where differences in opinion arise, the voting scheme employs reconciliation mechanisms to facilitate consensus building. This may include weighting systems to account for the relative importance of stakeholder perspectives, negotiation rounds to address conflicting viewpoints, or expert panels to provide additional insights and recommendations.

Step 6: Final decision and implementation: After thorough deliberation and consensus building, a final decision is reached regarding the adoption of healthcare interventions. The decision reflects the collective wisdom and input of all stakeholders involved. Implementation plans are developed to ensure effective execution of the chosen interventions, with ongoing monitoring and evaluation to assess their impact over time

4 Case Study: Assessing the Value of Reset-O for Opioid Use Disorder Treatment

4.1 case presentation.

The widespread occurrence of opioid use disorder (OUD) has placed significant burdens on both healthcare systems and society at large. This has triggered the need to develop alternative treatment approaches for patients suffering from OUD. reSET-O, a US FDA-approved prescription digital therapeutic, is now being considered for formulary placement by an employer-sponsored health plan in the United States (US). The employer-sponsored health plan is particularly interested in a comprehensive assessment of the broader impact and benefits associated with the utilization of reSET-O in combination with treatment as usual (TAU) when compared with TAU alone for its qualified members.

4.2 Methods

This case study employs a multipronged framework that encompasses DCEA implications and macroeconomic impacts to optimize resource allocation for OUD treatment, as previously described.

Step 1: Defining the population of interest: For this case study, the population comprised patients diagnosed with OUD who were eligible for treatment with the digital therapeutic application reSET-O. To capture the nuanced effects of socioeconomic status on the outcomes of the intervention, we stratified these patients into five quintiles based on their socioeconomic status. This is defined based on household income obtained from the US census data [ 39 ]. These quintiles ranged from the lowest to the highest, including second, middle, and fourth, thereby encompassing the full spectrum of socioeconomic status. This stratification allowed us to evaluate the distributional impact of the reSET-O treatment across different socioeconomic layers and to understand how the intervention could potentially ameliorate or exacerbate existing health inequalities among patients with OUD.

Step 2: Determining baseline and average QALE by socioeconomic status: The baseline data in Online Resource 2 eTable 1, illustrates the disparities in QALE across five socioeconomic groups before any intervention. The average QALE is set at 60 years, with the lowest quintile (Group 1) having a QALE of 40 years, indicating a significant disadvantage of 20 years compared with the average. Conversely, the top quintile (Group 5) enjoys a QALE 20 years above the average.

Step 3: Applying the inequality aversion parameter (α): In our analysis, we utilized estimates of 0.25 for the α parameter, which would typically be obtained through a survey of the general public in the US. Social welfare was derived by applying the Kolm index to the mean level of health in the distribution, resulting in the determination of the EDE level of health.

Step 4: Introducing the interventions and recalculating QALEs: With the introduction of health interventions, such as reSET-O + TAU, QALEs are recalculated for each socioeconomic group. These new QALE figures reflect the health benefits or losses each intervention provides (Online Resource 2 eTables 3 and 4).

Step 5: Reassessing inequality using the Kolm Index post-intervention: Post-intervention, the Kolm Index is employed to reassess inequality levels. This index is sensitive to changes in the distribution of health gains and losses. A decrease in the Kolm Index, as observed following the reSET-O + TAU intervention, signifies a reduction in health inequality (Online Resource 2 eTables 3 and 4).

Step 6: Computing the EDE post-intervention: Finally, the EDE health measure is computed to represent what the average QALE would be if health gains post-intervention were distributed equally among all groups. An increased EDE post-intervention value indicates a movement toward a more equitable health outcome distribution. An Excel (Office 365/Microsoft 365; Microsoft Corporation, Redmond, WA, USA) model that details the calculation steps described above is made available to readers as a companion file (Online Resource 2).

4.2.2 Extended HTA of reSET-O

We used an input-output model to understand how the reSET-O treatment for OUD affects the wider economy. This approach helps us see the full range of economic changes that healthcare treatments can bring about. In this analysis, we focus on value added, income and employment impacts. We break down this analysis into several clear steps:

Step 1: Choice of aggregation level: Our analysis is built on a 2021 SIOT, which dissects the economy into distinct sectors. This delineation includes critical sectors such as OUD subsectors, amended health sectors, and non-health-related sectors. The specificity of this categorization is achieved by using NAICS codes, enabling precise tracking of economic activities.

Step 2: Analyzing the characteristics of the SIOT: The SIOT, represented in Online Resource 3 eTable 1, is a matrix showcasing the flow of economic transactions between sectors. It captures both the intermediate demand (the goods and services exchanged for production) and the final demand (the consumption by end users such as households and government). This table culminates in a total output for each sector, summing up the intermediate and final demands.

Step 3: Calculating the Leontief inverse: At the core of our analysis is the Leontief Inverse, computed by first determining the technical coefficients in Online Resource 3 eTable 2. These coefficients, reflecting the intersector production dependencies, are obtained by dividing the intermediate demand by each sector’s total output. We then constructed an identity matrix, as seen in Online Resource 3 eTable 3, which, when combined with the technical coefficients, results in the Leontief Matrix (Online Resource 3 eTable 4). Subtracting the technical coefficients from the identity matrix provides us with the Leontief Inverse, a pivotal element that illustrates how sectoral changes propagate through the economy. Online Resource 3 eTable 5 showcases the direct, indirect, and total multiplier effects of economic activities. It illustrates how spending in one sector can amplify throughout the economy, significantly in non-health-related sectors with a total multiplier effect of 2.9304. Online Resource 3 eTable 6 offers employment figures, which, when juxtaposed with changes in final demand, estimate the potential employment impacts across sectors.

Step 4: Applying the input-output model using simulation scenarios: Employing the input-output model, we examine two scenarios. Initially, we establish a baseline scenario (TAU alone), calculated by multiplying the final demand by the output ratios from Online Resource 3 eTable 7. We then simulate an alternative scenario (reSET-O + TAU) in which we hypothesize a 15% reduction in final demand within the OUD sector, diverting these resources to non-health sectors. The implications of such a shift are quantified by examining the changes in value added, income, and employment, as captured in Table  3 . This comparative analysis allows us to unveil the net broader impacts of the reSET-O intervention, demonstrating its potential for enhancing economic value, augmenting income, and creating employment opportunities compared with the status quo. An Excel (Office 365/Microsoft 365) model that presents the calculation steps described above is made available to readers as a companion file (Online Resource 3).

4.2.3 Decision Rules through the Voting Scheme

The voting scheme consisted of the integration of the results of DCEA and macroeconomic impacts to provide decision makers with a comprehensive framework for informed decision making, as described in Sect. 3.4. Decision makers evaluating the body of evidence generated through this analysis would include patients, clinicians, researchers, and policymakers.

4.3 Results

Upon applying the reSET-O combined with TAU, we observe an improvement in the QALE of disadvantaged groups (Groups 1 and 2) [Online Resource 1 eTable 3], where the presence of disease is indicated. The QALE increases to an average of 66 years, and the exponential function reflects decreased inequality, as shown by the Kolm Index reduction to 9.9, from 13.9. The EDE post-intervention QALE also increases, demonstrating that this intervention improves both health outcomes and equity, with a significant impact of DCEA (19.9) over the unadjusted health value (30) [Online Resource 1 eTable 3].

Conversely, when TAU is applied without the reSET-O intervention, we see an increase in the QALE for the advantaged groups (Groups 4 and 5) who already had a higher baseline QALE (Online Resource 1 eTable 4). The intervention exacerbates health inequalities, as evidenced by the Kolm Index increase to 19.9, indicating that society’s aversion to inequality is not addressed. The EDE post-intervention QALE decreases for these groups, suggesting a regressive effect, where the wealthier benefit more, thus widening the health disparity gap. This is further supported by the negative impact of DCEA (−29.9), showing a loss in health value when distributional effects are considered (Online Resource 2 eTable 4). Furthermore, the analysis demonstrated that reSET-O + TAU had a more pronounced impact on QALE gains (10 EDE QALYs gained) compared with TAU alone (0.02 EDE QALYs gained) (Table  1 ). By quantifying the distribution of health gains and losses, the analysis highlights the potential equity-enhancing aspects of reSET-O + TAU in OUD treatment. These findings underscore the importance of considering equity considerations when evaluating the value of healthcare interventions.

The DCEA health value metric provides a nuanced view of the interventions’ effects, revealing that reSET-O + TAU is a more equitable approach that benefits the socioeconomically disadvantaged groups, whereas TAU alone favors the already advantaged, worsening health inequality. These findings underscore the importance of considering health equity in HTA decisions, as interventions can have divergent effects on different segments of the population.

4.3.2 Extended HTA of reSET-O

Table 2 represents the inverse of Leontief matrix used in the estimation of the broader impact of the reSET-O + TAU intervention. The results in Table  3 suggest an increase in value added to the economy that is substantial, amounting to $6.7 million due to the reSET-O + TAU intervention. This figure reflects the total economic value generated by the intervention, distributed over several sectors. Notably, one sector alone accounts for a significant portion of this increase, contributing $3.19 million. This uptick in value added is a clear indication of the intervention’s capacity to enhance the overall productivity and worth of the economy. As for income, the intervention has collectively raised earnings by $2.3 million across all sectors. This boost is highlighted by the largest single increase within a sector, which stands at $1.37 million. Such an increase in income underscores the potential of the reSET-O + TAU intervention to raise the financial well-being of individuals within the economy.

Employment also sees a beneficial impact from the intervention, with a creation of 11.7 jobs spread across the sectors. The most pronounced effect within a single sector has been the addition of 5.28 jobs. This growth in employment is indicative of the intervention’s role in job creation and its positive influence on the labor market. Through this nuanced and detailed approach, our input-output model transcends traditional economic assessments, enabling us to highlight the broader economic ramifications of reSET-O, thus providing valuable insights into its potential benefits for the broader economy.

4.3.3 Decision Rule Through Voting Scheme

The extended HTA analysis demonstrates positive macroeconomic impacts, including a positive net value added, an increase in income, and a labor force gain of 12 individuals (Table  3 ). These broader impacts contribute to the overall value of reSET-O. This placed reSET-O in the first quadrant of the voting scheme proposed. As a result of the decision conferencing, it was recommended that the employer-sponsored health plan should consider the implementation of reSET-O as part of a comprehensive treatment strategy for patients with OUD in their network. The intervention demonstrates cost effectiveness, enhances patient-centeredness, addresses equity concerns, and has positive macroeconomic impacts. These findings support the value and potential benefits of reSET-O in improving patient outcomes and optimizing resource allocation for OUD treatment.

5 Discussion

This manuscript presented a novel conceptual framework for the value assessment of healthcare interventions, aiming to address the limitations of current HTAs in capturing the broader economic and societal impacts beyond the healthcare sector. The framework combines DCEA and extended HTA analysis with input-output modeling, with a voting scheme to guide decision making.

The integration of DCEA into the framework allows for a more comprehensive evaluation of healthcare interventions by incorporating equity considerations and patient-centered outcomes. By assessing the impact of interventions on different population subgroups and evaluating disparities, DCEA enhances the understanding of the distributional impacts of interventions. This information is crucial for decision makers to ensure that the adoption of interventions does not exacerbate existing healthcare disparities, and is aligned with the goal of equitable access to effective healthcare. However, implementing DCEA requires access to reliable data on health outcomes, costs, and sociodemographic and equity variables, which may pose challenges in certain settings.

Furthermore, the inclusion of extended HTA analysis with input-output modeling is a significant advancement in capturing the broader economic impacts of healthcare interventions. By simulating the ripple effects on various economic sectors, such as changes in productivity, job creation, and innovation, this analysis provides a more holistic assessment of the societal consequences. This approach acknowledges that healthcare interventions have implications beyond the healthcare sector, impacting the overall economy and society. The input-output modeling technique offers valuable insights into the interconnectedness of industries and the potential economic impacts associated with healthcare interventions. However, it is important to note that implementing this analysis requires access to comprehensive and up-to-date economic data, which may pose challenges, particularly in resource-constrained settings.

To guide decision making, the proposed voting scheme combines the outputs of DCEA and extended HTA analysis, providing a practical approach that considers multiple dimensions and stakeholder perspectives. This inclusive approach helps balance the assessment of interventions by considering not only their health outcomes and economic impacts but also their broader societal implications. The voting scheme provides decision makers with a framework to guide the adoption of interventions based on a more comprehensive evaluation of their value. However, it is essential to ensure the representation of diverse stakeholders and their meaningful involvement in the decision-making process to avoid potential biases.

While the proposed framework shows promise, there are several challenges and considerations that need to be addressed. The availability and quality of data play a crucial role in the implementation of the framework. Reliable data on health outcomes, costs, equity-relevant sociodemographic variables, and intersectoral linkages are necessary to conduct robust assessments. Efforts should be made to improve data collection and sharing mechanisms to support the implementation of the framework in different settings. It is reassuring to see that the WHO has recently been leading the way by facilitating data collection through the released Health Inequality Data Repository (HIDR), the largest global database of publicly available disaggregated data on health inequality [ 40 ].

The integration of broader impacts beyond the health sector, stemming from the use of health technologies, necessitates careful consideration of ethical implications. Recognizing the fundamental role of ethics in healthcare, our approach adheres to key bioethical principles: justice, ensuring fair access and distribution of healthcare resources; autonomy, respecting patient choices and rights; beneficence, maximizing benefits while minimizing harm; and non-maleficence, avoiding harm to patients. The ethical challenges in our framework primarily revolve around potential conflicts between health and non-health sector impacts. For instance, while an intervention may demonstrate substantial non-health sector advantages, it must not compromise the quality of patient care or overlook the individual’s healthcare needs. This balancing act requires a nuanced understanding of both impacts and ethical priorities. To navigate these complexities, we propose developing clear guidelines that assist decision makers in evaluating healthcare interventions when broader impacts are considered. These guidelines should prioritize patient well-being, ensuring that economic considerations do not overshadow the fundamental goal of healthcare: to provide beneficial and harm-free treatment to patients. Moreover, the inclusion of stakeholder perspectives, including patients, healthcare providers, and policymakers, is crucial in ensuring that the framework remains aligned with ethical values and societal expectations. Moreover, our framework aims to address healthcare disparities without favoring one group over another, fostering equity. Equity considerations are essential in decision making but are not the sole factor. Thresholds for prioritizing equity are context-dependent and ethically guided. The complexity of implementing a voting scheme to reflect societal values is acknowledged, but its significance in democratizing decision making is emphasized. This approach begins a conversation for a more inclusive healthcare evaluation, highlighting areas for further research and refinement in our framework. In summary, our framework seeks not only to advance healthcare evaluation by incorporating economic aspects but also to uphold the highest ethical standards, ensuring that economic benefits enhance rather than detract from the primary objective of healthcare—improving patient outcomes.

Our framework’s success hinges on interdisciplinary collaboration, uniting health economics, social sciences, and macroeconomics. This collaboration, involving diverse professionals, is key to effectively implementing and interpreting the framework’s results. Establishing collaborative platforms, such as interdisciplinary committees or working groups, allows professionals to regularly meet, discuss, and align on methodologies. These platforms would serve as hubs for sharing knowledge, debating ideas, and formulating collective strategies. Additionally, we recommend the development of structured interdisciplinary training programs and workshops that focus on sharing knowledge and techniques across different fields. These training programs would aim to build a common language and understanding among various professionals involved in HTA, facilitating better communication and collaboration. Finally, our approach includes regular review and updates of collaborative strategies based on feedback and outcomes, ensuring continuous improvement and alignment with the latest research and practices in HTA. This dynamic approach ensures that our framework remains relevant and effective in the ever-evolving field of healthcare. It brings together concepts and methods from health economics, social sciences, and macroeconomics. Collaboration between researchers, policymakers, economists, and healthcare professionals is essential to ensure the successful implementation and interpretation of the framework’s results. Building capacity and fostering partnerships across disciplines will be vital in advancing the field of comprehensive value assessment. To ensure its full adoption, the framework needs to be further validated and tested in various healthcare contexts and settings. Case studies, pilot projects, and sensitivity analyses can help assess the feasibility, reliability, and generalizability of the framework. By applying the framework to different healthcare interventions and comparing the results with traditional HTAs, its added value and potential for informing decision making can be better understood.

In Sect.  2 of our manuscript, we explore the limitations of contemporary HTA and acknowledge that certain impacts, such as environmental pollution and resource depletion, are not directly addressed in our proposed conceptual framework. We want to emphasize however that the importance of these issues is not diminished in our analysis. Our framework is designed to be both innovative and encompassing. Incorporating environmental impacts into broader evaluations could significantly enhance the assessment landscape. By employing the input-output model as a key methodological tool, our framework could integrate these impacts more fully. This would involve a detailed calibration of technical coefficients within our framework, specifically adjusted to account for the environmental effects on the economic sector’s interconnections. Such an approach not only highlights the critical role of environmental considerations but also enhances the precision and relevance of our evaluations. It ensures a thorough comprehension of the complex interplay between ecological factors and economic dynamics, affirming our commitment to a holistic and forward-thinking assessment methodology.

6 Conclusion

This paper’s proposed conceptual framework presents a significant advancement in improving HTAs to account for the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make more informed choices that align with societal priorities and promote equitable access to effective healthcare interventions. However, further research, collaboration, and validation are needed to fully realize the potential of this framework and ensure its practical applicability in diverse healthcare contexts.

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The authors express their sincere appreciation to the Innovation and Value Initiative (IVI) for extending a gracious invitation to take part in the Valuing Innovation Project call for papers competition.

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Ali, A.A., Kulkarni, A., Bhattacharjee, S. et al. Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework. PharmacoEconomics (2024). https://doi.org/10.1007/s40273-024-01392-w

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