research paper conceptual framework in research

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.

What is a good example of a conceptual framework?

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18 April 2023

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Miroslav Damyanov

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• The importance of a conceptual framework

The main purpose of a conceptual framework is to improve the quality of a research study. A conceptual framework achieves this by identifying important information about the topic and providing a clear roadmap for researchers to study it.

Through the process of developing this information, researchers will be able to improve the quality of their studies in a few key ways.

Clarify research goals and objectives

A conceptual framework helps researchers create a clear research goal. Research projects often become vague and lose their focus, which makes them less useful. However, a well-designed conceptual framework helps researchers maintain focus. It reinforces the project’s scope, ensuring it stays on track and produces meaningful results.

Provide a theoretical basis for the study

Forming a hypothesis requires knowledge of the key variables and their relationship to each other. Researchers need to identify these variables early on to create a conceptual framework. This ensures researchers have developed a strong understanding of the topic before finalizing the study design. It also helps them select the most appropriate research and analysis methods.

Guide the research design

As they develop their conceptual framework, researchers often uncover information that can help them further refine their work.

Here are some examples:

Confounding variables they hadn’t previously considered

Sources of bias they will have to take into account when designing the project

Whether or not the information they were going to study has already been covered—this allows them to pivot to a more meaningful goal that brings new and relevant information to their field

• Steps to develop a conceptual framework

There are four major steps researchers will follow to develop a conceptual framework. Each step will be described in detail in the sections that follow. You’ll also find examples of how each might be applied in a range of fields.

Step 1: Choose the research question

The first step in creating a conceptual framework is choosing a research question . The goal of this step is to create a question that’s specific and focused.

By developing a clear question, researchers can more easily identify the variables they will need to account for and keep their research focused. Without it, the next steps will be more difficult and less effective.

Here are some examples of good research questions in a few common fields:

Natural sciences: How does exposure to ultraviolet radiation affect the growth rate of a particular type of algae?

Health sciences: What is the effectiveness of cognitive-behavioral therapy for treating depression in adolescents?

Business: What factors contribute to the success of small businesses in a particular industry?

Education: How does implementing technology in the classroom impact student learning outcomes?

Step 2: Select the independent and dependent variables

Once the research question has been chosen, it’s time to identify the dependent and independent variables .

The independent variable is the variable researchers think will affect the dependent variable . Without this information, researchers cannot develop a meaningful hypothesis or design a way to test it.

The dependent and independent variables for our example questions above are:

Natural sciences

Independent variable: exposure to ultraviolet radiation

Dependent variable: the growth rate of a particular type of algae

Health sciences

Independent variable: cognitive-behavioral therapy

Dependent variable: depression in adolescents

Independent variables: factors contributing to the business’s success

Dependent variable: sales, return on investment (ROI), or another concrete metric

Independent variable: implementation of technology in the classroom

Dependent variable: student learning outcomes, such as test scores, GPAs, or exam results

Step 3: Visualize the cause-and-effect relationship

This step is where researchers actually develop their hypothesis. They will predict how the independent variable will impact the dependent variable based on their knowledge of the field and their intuition.

With a hypothesis formed, researchers can more accurately determine what data to collect and how to analyze it. They will then visualize their hypothesis by creating a diagram. This visualization will serve as a framework to help guide their research.

The diagrams for our examples might be used as follows:

Natural sciences : how exposure to radiation affects the biological processes in the algae that contribute to its growth rate

Health sciences : how different aspects of cognitive behavioral therapy can affect how patients experience symptoms of depression

Business : how factors such as market demand, managerial expertise, and financial resources influence a business’s success

Education : how different types of technology interact with different aspects of the learning process and alter student learning outcomes

Step 4: Identify other influencing variables

The independent and dependent variables are only part of the equation. Moderating, mediating, and control variables are also important parts of a well-designed study. These variables can impact the relationship between the two main variables and must be accounted for.

A moderating variable is one that can change how the independent variable affects the dependent variable. A mediating variable explains the relationship between the two. Control variables are kept the same to eliminate their impact on the results. Examples of each are given below:

Moderating variable: water temperature (might impact how algae respond to radiation exposure)

Mediating variable: chlorophyll production (might explain how radiation exposure affects algae growth rate)

Control variable: nutrient levels in the water

Moderating variable: the severity of depression symptoms at baseline might impact how effective the therapy is for different adolescents

Mediating variable: social support might explain how cognitive-behavioral therapy leads to improvements in depression

Control variable: other forms of treatment received before or during the study

Moderating variable: the size of the business (might impact how different factors contribute to market share, sales, ROI, and other key success metrics)

Mediating variable: customer satisfaction (might explain how different factors impact business success)

Control variable: industry competition

Moderating variable: student age (might impact how effective technology is for different students)

Mediating variable: teacher training (might explain how technology leads to improvements in learning outcomes)

Control variable: student learning style

• Conceptual versus theoretical frameworks

Although they sound similar, conceptual and theoretical frameworks have different goals and are used in different contexts. Understanding which to use will help researchers craft better studies.

Conceptual frameworks describe a broad overview of the subject and outline key concepts, variables, and the relationships between them. They provide structure to studies that are more exploratory in nature, where the relationships between the variables are still being established. They are particularly helpful in studies that are complex or interdisciplinary because they help researchers better organize the factors involved in the study.

Theoretical frameworks, on the other hand, are used when the research question is more clearly defined and there’s an existing body of work to draw upon. They define the relationships between the variables and help researchers predict outcomes. They are particularly helpful when researchers want to refine the existing body of knowledge rather than establish it.

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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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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?

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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Defining The Conceptual Framework

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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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How To Make Conceptual Framework (With Examples and Templates)

We all know that a research paper has plenty of concepts involved. However, a great deal of concepts makes your study confusing.

A conceptual framework ensures that the concepts of your study are organized and presented comprehensively. Let this article guide you on how to make the conceptual framework of your study.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

At a glance: free conceptual framework templates.

Too busy to create a conceptual framework from scratch? No problem. We’ve created templates for each conceptual framework so you can start on the right foot. All you need to do is enter the details of the variables. Feel free to modify the design according to your needs. Please read the main article below to learn more about the conceptual framework.

Conceptual Framework Template #1: Independent-Dependent Variable Model

Conceptual framework template #2: input-process-output (ipo) model, conceptual framework template #3: concept map, what is a conceptual framework.

A conceptual framework shows the relationship between the variables of your study.  It includes a visual diagram or a model that summarizes the concepts of your study and a narrative explanation of the model presented.

Why Should Research Be Given a Conceptual Framework?

Imagine your study as a long journey with the research result as the destination. You don’t want to get lost in your journey because of the complicated concepts. This is why you need to have a guide. The conceptual framework keeps you on track by presenting and simplifying the relationship between the variables. This is usually done through the use of illustrations that are supported by a written interpretation.

Also, people who will read your research must have a clear guide to the variables in your study and where the research is heading. By looking at the conceptual framework, the readers can get the gist of the research concepts without reading the entire study. 

Related: How to Write Significance of the Study (with Examples)

What Is the Difference Between Conceptual Framework and Theoretical Framework?

Both of them show concepts and ideas of your study. The theoretical framework presents the theories, rules, and principles that serve as the basis of the research. Thus, the theoretical framework presents broad concepts related to your study. On the other hand, the conceptual framework shows a specific approach derived from the theoretical framework. It provides particular variables and shows how these variables are related.

Let’s say your research is about the Effects of Social Media on the Political Literacy of College Students. You may include some theories related to political literacy, such as this paper, in your theoretical framework. Based on this paper, political participation and awareness determine political literacy.

For the conceptual framework, you may state that the specific form of political participation and awareness you will use for the study is the engagement of college students on political issues on social media. Then, through a diagram and narrative explanation, you can show that using social media affects the political literacy of college students.

What Are the Different Types of Conceptual Frameworks?

The conceptual framework has different types based on how the research concepts are organized 1 .

1. Taxonomy

In this type of conceptual framework, the phenomena of your study are grouped into categories without presenting the relationship among them. The point of this conceptual framework is to distinguish the categories from one another.

2. Visual Presentation

In this conceptual framework, the relationship between the phenomena and variables of your study is presented. Using this conceptual framework implies that your research provides empirical evidence to prove the relationship between variables. This is the type of conceptual framework that is usually used in research studies.

3. Mathematical Description

In this conceptual framework, the relationship between phenomena and variables of your study is described using mathematical formulas. Also, the extent of the relationship between these variables is presented with specific quantities.

How To Make Conceptual Framework: 4 Steps

1. identify the important variables of your study.

There are two essential variables that you must identify in your study: the independent and the dependent variables.

An independent variable is a variable that you can manipulate. It can affect the dependent variable. Meanwhile, the dependent variable is the resulting variable that you are measuring.

You may refer to your research question to determine your research’s independent and dependent variables.

Suppose your research question is: “Is There a Significant Relationship Between the Quantity of Organic Fertilizer Used and the Plant’s Growth Rate?” The independent variable of this study is the quantity of organic fertilizer used, while the dependent variable is the plant’s growth rate.

2. Think About How the Variables Are Related

Usually, the variables of a study have a direct relationship. If a change in one of your variables leads to a corresponding change in another, they might have this kind of relationship.

However, note that having a direct relationship between variables does not mean they already have a cause-and-effect relationship 2 . It takes statistical analysis to prove causation between variables.

Using our example earlier, the quantity of organic fertilizer may directly relate to the plant’s growth rate. However, we are not sure that the quantity of organic fertilizer is the sole reason for the plant’s growth rate changes.

3. Analyze and Determine Other Influencing Variables

Consider analyzing if other variables can affect the relationship between your independent and dependent variables 3 .

4. Create a Visual Diagram or a Model

Now that you’ve identified the variables and their relationship, you may create a visual diagram summarizing them.

Usually, shapes such as rectangles, circles, and arrows are used for the model. You may create a visual diagram or model for your conceptual framework in different ways. The three most common models are the independent-dependent variable model, the input-process-output (IPO) model, and concept maps.

a. Using the Independent-Dependent Variable Model

You may create this model by writing the independent and dependent variables inside rectangles. Then, insert a line segment between them, connecting the rectangles. This line segment indicates the direct relationship between these variables. 

Below is a visual diagram based on our example about the relationship between organic fertilizer and a plant’s growth rate. 

b. Using the Input-Process-Output (IPO) Model

If you want to emphasize your research process, the input-process-output model is the appropriate visual diagram for your conceptual framework.

To create your visual diagram using the IPO model, follow these steps:

• Determine the inputs of your study . Inputs are the variables you will use to arrive at your research result. Usually, your independent variables are also the inputs of your research. Let’s say your research is about the Level of Satisfaction of College Students Using Google Classroom as an Online Learning Platform. You may include in your inputs the profile of your respondents and the curriculum used in the online learning platform.
• Outline your research process. Using our example above, the research process should be like this: Data collection of student profiles → Administering questionnaires → Tabulation of students’ responses → Statistical data analysis.
• State the research output . Indicate what you are expecting after you conduct the research. In our example above, the research output is the assessed level of satisfaction of college students with the use of Google Classroom as an online learning platform.
• Create the model using the research’s determined input, process, and output.

Presented below is the IPO model for our example above.

c. Using Concept Maps

If you think the two models presented previously are insufficient to summarize your study’s concepts, you may use a concept map for your visual diagram.

A concept map is a helpful visual diagram if multiple variables affect one another. Let’s say your research is about Coping with the Remote Learning System: Anxiety Levels of College Students. Presented below is the concept map for the research’s conceptual framework:

5. Explain Your Conceptual Framework in Narrative Form

Provide a brief explanation of your conceptual framework. State the essential variables, their relationship, and the research outcome.

Using the same example about the relationship between organic fertilizer and the growth rate of the plant, we can come up with the following explanation to accompany the conceptual framework:

Figure 1 shows the Conceptual Framework of the study. The quantity of the organic fertilizer used is the independent variable, while the plant’s growth is the research’s dependent variable. These two variables are directly related based on the research’s empirical evidence.

Conceptual Framework in Quantitative Research

You can create your conceptual framework by following the steps discussed in the previous section. Note, however, that quantitative research has statistical analysis. Thus, you may use arrows to indicate a cause-and-effect relationship in your model. An arrow implies that your independent variable caused the changes in your dependent variable.

Usually, for quantitative research, the Input-Process-Output model is used as a visual diagram. Here is an example of a conceptual framework in quantitative research:

Research Topic : Level of Effectiveness of Corn (Zea mays) Silk Ethanol Extract as an Antioxidant

Conceptual Framework in Qualitative Research

Again, you can follow the same step-by-step guide discussed previously to create a conceptual framework for qualitative research. However, note that you should avoid using one-way arrows as they may indicate causation . Qualitative research cannot prove causation since it uses only descriptive and narrative analysis to relate variables.

Here is an example of a conceptual framework in qualitative research:

Research Topic : Lived Experiences of Medical Health Workers During Community Quarantine

Conceptual Framework Examples

Presented below are some examples of conceptual frameworks.

Research Topic : Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract in the Blood Glucose Level of Swiss Mice (Mus musculus)

Figure 1 presents the Conceptual Framework of the study. The quantity of gabi leaf extract is the independent variable, while the Swiss mice’s blood glucose level is the study’s dependent variable. This study establishes a direct relationship between these variables through empirical evidence and statistical analysis . 

Research Topic : Level of Effectiveness of Using Social Media in the Political Literacy of College Students

Figure 1 shows the Conceptual Framework of the study. The input is the profile of the college students according to sex, year level, and the social media platform being used. The research process includes administering the questionnaires, tabulating students’ responses, and statistical data analysis and interpretation. The output is the effectiveness of using social media in the political literacy of college students.

Research Topic: Factors Affecting the Satisfaction Level of Community Inhabitants

Figure 1 presents a visual illustration of the factors that affect the satisfaction level of community inhabitants. As presented, environmental, societal, and economic factors influence the satisfaction level of community inhabitants. Each factor has its indicators which are considered in this study.

Tips and Warnings

• Please keep it simple. Avoid using fancy illustrations or designs when creating your conceptual framework. 
• Allot a lot of space for feedback. This is to show that your research variables or methodology might be revised based on the input from the research panel. Below is an example of a conceptual framework with a spot allotted for feedback.

Frequently Asked Questions

1. how can i create a conceptual framework in microsoft word.

First, click the Insert tab and select Shapes . You’ll see a wide range of shapes to choose from. Usually, rectangles, circles, and arrows are the shapes used for the conceptual framework. 

Next, draw your selected shape in the document.

Insert the name of the variable inside the shape. You can do this by pointing your cursor to the shape, right-clicking your mouse, selecting Add Text , and typing in the text.

Repeat the same process for the remaining variables of your study. If you need arrows to connect the different variables, you can insert one by going to the Insert tab, then Shape, and finally, Lines or Block Arrows, depending on your preferred arrow style.

2. How to explain my conceptual framework in defense?

If you have used the Independent-Dependent Variable Model in creating your conceptual framework, start by telling your research’s variables. Afterward, explain the relationship between these variables. Example: “Using statistical/descriptive analysis of the data we have collected, we are going to show how the <state your independent variable> exhibits a significant relationship to <state your dependent variable>.”

On the other hand, if you have used an Input-Process-Output Model, start by explaining the inputs of your research. Then, tell them about your research process. You may refer to the Research Methodology in Chapter 3 to accurately present your research process. Lastly, explain what your research outcome is.

Meanwhile, if you have used a concept map, ensure you understand the idea behind the illustration. Discuss how the concepts are related and highlight the research outcome.

3. In what stage of research is the conceptual framework written?

The research study’s conceptual framework is in Chapter 2, following the Review of Related Literature.

4. What is the difference between a Conceptual Framework and Literature Review?

The Conceptual Framework is a summary of the concepts of your study where the relationship of the variables is presented. On the other hand, Literature Review is a collection of published studies and literature related to your study. 

Suppose your research concerns the Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract on Swiss Mice (Mus musculus). In your conceptual framework, you will create a visual diagram and a narrative explanation presenting the quantity of gabi leaf extract and the mice’s blood glucose level as your research variables. On the other hand, for the literature review, you may include this study and explain how this is related to your research topic.

5. When do I use a two-way arrow for my conceptual framework?

You will use a two-way arrow in your conceptual framework if the variables of your study are interdependent. If variable A affects variable B and variable B also affects variable A, you may use a two-way arrow to show that A and B affect each other.

Suppose your research concerns the Relationship Between Students’ Satisfaction Levels and Online Learning Platforms. Since students’ satisfaction level determines the online learning platform the school uses and vice versa, these variables have a direct relationship. Thus, you may use two-way arrows to indicate that the variables directly affect each other.

• Conceptual Framework – Meaning, Importance and How to Write it. (2020). Retrieved 27 April 2021, from https://afribary.com/knowledge/conceptual-framework/
• Correlation vs Causation. Retrieved 27 April 2021, from https://www.jmp.com/en_ph/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
• Swaen, B., & George, T. (2022, August 22). What is a conceptual framework? Tips & Examples. Retrieved December 5, 2022, from https://www.scribbr.com/methodology/conceptual-framework/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

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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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Article Contents

Conceptual framework, data and methodology, general discussion, data collection statement, author notes.

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Consumer Trust: Meta-Analysis of 50 Years of Empirical Research

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Mansur Khamitov, Koushyar Rajavi, Der-Wei Huang, Yuly Hong, Consumer Trust: Meta-Analysis of 50 Years of Empirical Research, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 7–18, https://doi.org/10.1093/jcr/ucad065

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Trust is one of the highly important concepts of consumer research; yet it is characterized by a striking lack of generalizations and consensus regarding the relative strength of its antecedents, consequences, and moderators. To close this important gap, the current research reports a comprehensive large-scale meta-analysis shedding light on a wide variety of the antecedents, consequences, and moderators of the individual consumer’s trust and their relative importance. Empirical generalizations are based on 2,147 effect sizes from 549 studies across 469 manuscripts in numerous disciplines, representing a total of 324,834 respondents in 71 countries over a five-decade span (1970–2020). The key findings are thus that (1) integrity-based (vs. reliability-based) antecedents are more effective in driving trust, and (2) trust is more effective in improving primarily attitudinal (vs. primarily behavioral) outcomes. Moderation analyses unpack further heterogeneity. Notably, both integrity-based and reliability-based antecedents have become stronger drivers of consumer trust in recent years. Theoretical and practical contributions are discussed in addition to advancing important future directions.

Trust is one of the highly important concepts of consumer research. Trust is crucial in all aspects of our daily lives, such as commercial and social transactions, because it reduces perceived uncertainty regarding intentions and capabilities of other entities. Past research in marketing recognizes the significance of trust—albeit with a slightly richer tradition in the B2B setting, from extensive study of the nature of trust between business customers, to the point that two meta-analyses on that topic have emerged ( Geyskens, Steenkamp, and Kumar 1998 ; Palmatier et al. 2006 ). Given the synthesized and illustrious evidence accumulated in the B2B setting, it is puzzling that no systematic meta-analysis was conducted on the nuanced role of the individual consumer’s trust. 1

RQ1: What is the (relative) impact of a broad set of antecedents of consumer trust? RQ2: Under what conditions do antecedents of consumer trust become more effective?

RQ3: What is the (relative) impact of consumer trust on a broad set of downstream consequences?

Overall, our meta-analysis of 2,147 individual effects derived from 549 studies across 469 manuscripts from 1970 to 2020 offers generalizable insights into antecedents and consequences of consumer trust, along with future implications. The work provides a big-tent investigation of consumer-trust research that highlights its multi-disciplinary nature using the meta-analytic lens.

Consumer trust is defined as “a consumer’s confidence in […] reliability and integrity” of the target of trust ( De Wulf, Odekerken-Schröder, and Iacobucci 2001 , 36). In order to identify drivers of consumer trust, it is important to consider what trust consists of. While there are small differences in how consumer trust is conceptualized in past research, it is commonly accepted that it encompasses consumers’ beliefs about how reliably and with integrity an entity would deliver on its stated promise(s) ( Garbarino and Johnson 1999 ; McKnight, Choudhury, and Kacmar 2002 ). Thus, factors that drive inferences of an entity’s reliability or integrity are particularly relevant in generating consumer trust. That is, a systematic classification of prior consumer trust literature simply cannot be considered complete without accounting for both integrity-based trust antecedents (IBTA) and reliability-based trust antecedents (RBTA). Theoretical support for this underlying grouping can be found in numerous seminal consumer trust papers: intentions toward the consumers versus reliability ( Delgado‐Ballester and Munuera‐Alemán 2001 ), benevolence and integrity versus ability and dependability ( Sirdeshmukh, Singh, and Sabol 2002 ), honesty versus reliability and safety ( Chaudhuri and Holbrook 2001 ), can be counted on to be good to the consumer versus confidence and reliability ( Garbarino and Johnson 1999 ), and behaving in the long-term interest of the customer versus confidence and reliability ( Crosby, Evans, and Cowles 1990 ). 2 , 3 Furthermore, our review of past studies on consumer trust led us to two general groups of outcomes associated with consumer trust: primarily attitudinal consequences (PAC) and primarily behavioral consequences (PBC).

After determining general groups of antecedents and consequences based on past research, we chose specific antecedents and consequences based on (1) how frequently a construct appears in past research on individual consumer’s trust and (2) whether a construct fits our theoretical framework (e.g., IBTA or RBTA for antecedents). 4 Therefore, we objectively focus on the most prevalent antecedents and consequences of consumer trust, as determined by past research. In doing so, in accordance with Palmatier et al. (2006) , we retain antecedents that appear in at least 10% of the past studies on consumer trust. 5

These considerations led us to eight antecedents of consumer trust: IBTA include three constructs (attachment, ethicality and social responsibility [SR], reputation) and RBTA encompass five constructs (marketing investments, perceived value, competence, perceived risk, perceived quality). 6 For the nine consequences in our study, PAC include five constructs (self-concept connection, evaluation, engagement, attitudinal loyalty, satisfaction) and PBC have four constructs (behavioral loyalty, willingness to pay, purchase intention, market performance). 7

In web appendix A , we define and describe these constructs, report their common aliases, and highlight sample studies that examined their respective relationship with consumer trust. Figure 1 illustrates our theoretical framework. We next briefly discuss how and why the antecedents fit within the two buckets and how they affect consumer trust (for a more detailed review of research on drivers of consumer trust, see web appendix B ). The discussion on the relationships between consumer trust and its consequences can be found in web appendix C .

THEORETICAL FRAMEWORK

Antecedents of Consumer Trust

Turning to how the eight underlying antecedents fit within the two buckets and in turn drive consumer trust, we start with the three IBTA. Through ongoing encounters and interactions, consumers often form a connection with the business entity and develop attachment to the entity, which has been shown to impact consumer trust ( Bidmon 2017 ) by affecting consumers’ perceptions of the sincere relational motives of the cherished entity ( Khamitov et al. 2019 ).

Growing consumer consciousness in the 21st century has encouraged businesses to focus on ethicality and SR . The ethicality of a business entity (i.e., the commitment to doing the right thing) and investments in CSR activities influence consumers’ trust by signaling to them that the entity is moral, honest, benevolent, less likely to cheat, and likely to be of high integrity ( Diallo and Lambey-Checchin 2017 ). Relatedly, the reputation of a business entity—being highly respected and getting known for having the consumer’s best interests at heart—has been shown to significantly enhance consumer trust ( Johnson and Grayson 2005 ).

In terms of the five RBTA, businesses invest in various marketing activities to create and communicate value and expertise to their consumers. Different forms of sale-independent marketing investments influence consumer trust through conveying capability (e.g., signaling superiority; Rajavi, Kushwaha, and Steenkamp 2019 ). Perceived value has been shown to affect consumer trust by making consumers presume that the entity has the reliability and resources to come up with offerings that provide superior value to them ( Wu and Huang 2023 ).

Consumers’ interactions with a business entity, and the information that consumers obtain via different sources (e.g., news, social media) affect customers’ beliefs about competence , perceived quality , and perceived risk of these entities. Competence affects consumer trust by influencing perceptions regarding the entity’s ability to deliver and reliably satisfy consumers’ needs ( Sung and Kim 2010 ). Perceived quality drives trust by enhancing perceptions regarding the overall excellence of an offering and improving public assessments of its attributes ( Hennig-Thurau, Langer, and Hansen 2001 ). Finally, perceived risk can erode consumers’ beliefs regarding the likelihood that the business entity will reliably fulfill its promises, because an entity that increases perceived risk for consumers sends negative signals about its ability to deliver ( Pappas 2016 ).

According to the considerable amount of work focused on morality in the marketplace ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller et al. 2022 ), a vast majority of ordinary consumers are guided by moral beliefs and intuitions in the marketplace, with a huge importance placed on marketplace actors acting responsibly and with integrity, making integrity-related levers particularly influential when it comes to consumer trust. We thus posit antecedents based on integrity, on average, outperform antecedents based on reliability in driving trust.

In light of the seeming evidence that consumer trust has undergone dramatic changes in recent times ( Edelman 2021 ; Gallup 2023 ; Khamitov et al. 2019 ), we utilize year of publication to examine how the impacts of trust antecedents have changed recently. Additionally, given that the research in the consumer-trust domain has evolved from an early focus on brands and firms ( Garbarino and Johnson 1999 ) to encompass trust toward specific offerings ( Johnson and Grayson 2005 ), industries ( Diallo and Lambey-Checchin 2017 ), and even technologies ( Kim and Peterson 2017 ), we include a target of trust moderator to unpack this heterogeneity. Lastly, as extant work hinted at the potential moderating role of search, experience, and credence attributes in the context of consumer trust ( Pan and Chiou 2011 ), we employ a type of attribute moderator.

Overview of Data Collection and Coding Procedure

To ensure extensive coverage of articles that examined drivers or consequences of the individual consumer’s trust, we systematically searched several databases, including Google Scholar, ProQuest, EBSCOhost, and Web of Science. We used individual keyword phrases and their combinations such as “consumer trust,” “firm trust,” “customer mistrust,” etc., to identify studies related to consumer trust (see web appendix D for the complete list of keywords). We also manually reviewed leading journals in marketing and other disciplines to uncover additional work. We retained studies that (1) examined the individual consumer’s trust rather than an organization’s, (2) were published between 1970 and 2020, (3) had an empirical focus, and (4) reported sufficient information for direct use or indirect computation of our focal effects. We also made an effort to incorporate unpublished work (“file drawer”) by soliciting unpublished manuscripts in a blind, anonymous, confidential manner via the Association for Consumer Research’s ACR-L and American Marketing Association’s ELMAR listservs over a period of four weeks. This led to a final sample that includes 2,147 effect sizes from 549 studies across 469 manuscripts, representing a total of 324,834 respondents in 71 countries over a five-decade span. 8 Our benchmarking review of consumer research suggests that our final dataset’s scope and magnitude compare very favorably to those of other recent meta-analytic datasets (290 studies in Khamitov et al. 2019 ; 141 studies in Weingarten and Goodman 2021 ). 9

Following other meta-analytic studies ( Gremler et al. 2020 ; Palmatier et al. 2006 ), we use Pearson’s correlation coefficient as the focal effect size metric in our study. As needed, we employed conversion formulas to transform other available statistics into correlation coefficients ( Lipsey and Wilson 2001 ). We adjusted the effect sizes for measurement error using the square root of the products of the reliabilities of the two constructs, that is, consumer trust and its respective antecedent or consequence ( Hunter and Schmidt 1990 ). Finally, we weighted the resulting reliability-adjusted correlations by sample size ( Hunter and Schmidt 1990 ). For a detailed description of our data collection (i.e., literature search, inclusion criteria, PRISMA flow chart of the screening process and outcomes, coding procedure, control variables), see web appendix D .

Methodology: Hierarchical Linear Modeling

Following Raudenbush and Bryk’s (2001) recommendation, we specify a three-level hierarchical linear model (HLM) that accounts for the nested structure of data. The first level represents observations belonging to each study (i.e., the within-study effect sizes), the second level stands for different studies belonging to a paper, and the third level incorporates the distinct papers in our dataset. In our HLM model with maximum-likelihood estimation, the dependent variable represents adjusted and weighted effect sizes (correlations). The focal independent variables are eight dummies corresponding to the eight antecedents of consumer trust (RQ1). Following Gremler et al. (2020) , for each effect size, we set all dummy variables to 0, except the dummy variable corresponding to the antecedent of consumer trust, whose correlation the focal effect size is capturing (it gets a value of 1). We also control for several sample-, study-, and paper-level characteristics that we briefly discuss in the Discussion section. Additionally, we present the moderator subgroup analyses (year of publication, target of trust, type of attribute) to decompose heterogeneity (RQ2). In web appendices E and F , we detail our model specifications as well as robustness checks and publication bias analyses/corrections. We use a similar three-level HLM to examine the consequences of consumer trust.

Antecedents of Consumer Trust (RQ1)

The antecedent results appear in table 1 . The focal effects are robust to inclusion or exclusion of covariates in models A0 and A1. We focus on the results from the full model (A1).

RESULTS FOR ANTECEDENTS OF CONSUMER TRUST

p < .05;

p < .01;

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, magnitude of estimates are directly comparable across the antecedents of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the three integrity-based (the five reliability-based) antecedents to construct the aggregate variables. For the aggregate analyses presented here and in subsequent analyses, we utilize absolute values of effect sizes since some effect sizes are positive and others are negative. The estimates for the covariates and the deviance values are based on models with the eight antecedents included.

Most importantly, when we compare aggregated integrity-based antecedents with aggregated reliability-based ones, we observe a stronger magnitude for integrity-based antecedents ( b IBTA = 0.432, SE = 0.021 vs. b RBTA = 0.353, SE = 0.014, p = .002). That is, integrity-based antecedents have stronger influence on consumer trust than reliability-based antecedents. Thus, the most important aspect of trust building is establishing and conveying integrity and honesty aligned with morality in the marketplace stream ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller, Teeny, and Petty 2022 ), central premise of which is that consumers perceive and care about the business morality.

When looking at specific antecedents of trust, reputation emerges as the strongest driver ( b = 0.460, SE = 0.031, p < .001), followed by ethicality and SR ( b = 0.426, SE = 0.032, p < .001). Reputation is likely the strongest driver of consumer trust, since reputation is and has for a while been the most valuable marketplace currency according to the notion of reputation economy ( Rifkin, Corus, and Kirk 2022 ), which underscores that the consumption marketplace is an environment where trust toward firms and brands is built on reputational considerations of track record and the promise(s) they deliver. The ethicality and SR results are in line with the importance and relevance of moral theories and concepts in marketplace environment ( Diallo and Lambey-Checchin 2017 ) and are consistent with the theme of a recent issue of JCP on marketplace morality ( Campbell and Winterich 2018 ).

The next three antecedents, while relatively weaker in terms of their strength, also emerge as strong and positive: attachment ( b = 0.408, SE = 0.030, p < .001), perceived quality ( b = 0.407, SE = 0.034, p < .001), and perceived value ( b = 0.353, SE = 0.021, p < .001). Attachment’s strong effect reinforces consumer–brand relationship theory as it pertains to attachment figures ( Khamitov et al. 2019 ). The quality finding reinforces the relationship marketing theories ( Palmatier et al. 2006 ), whereas the relatively strong positive effect for perceived value highlights the importance of ensuring that consumer needs and wants are fulfilled. Marketing investments ( b = 0.256, SE = 0.032, p < .001), competence ( b = 0.209, SE = 0.087, p = .016), and the non-significant perceived risk ( b  =   −0.120, SE = 0.073, p = .102) are the weakest drivers of consumer trust. The last finding is especially surprising given that risk is traditionally strongly linked to trust in the extant consumer-trust literature ( Elliott and Yannopoulou 2007 ). This outcome suggests that the identified risk is a weaker determinant of trust than expected, likely because a vast majority of consumption situations in our meta-analytic dataset entail minimal levels of risk 10 ; hence, the risk-reducing capability may not be particularly relevant when it comes to driving trust. 11

Moderating Conditions (RQ2)

Trust across time.

We conducted the year of publication moderation analyses on antecedents of consumer trust by comparing meta-analytic coefficients in recent studies (published after 2015) versus older studies (published before 2015). 12 We conjectured that the change in trends in older versus more recent studies would be manifested in the future: antecedents that have recently become stronger determinants of trust will continue to play an even more important role in the future. We present the detailed results in table 2 . We find that the magnitude of the effectiveness of IBTA ( p = .031) and RBTA ( p = .033) has both significantly increased over time, although less so for the RBTA. That is, different antecedents of trust are more effective in driving consumer trust in today’s marketplace than in the past. This outcome is consistent with the observation of consumer scholars that the roles of trust and other consumer relationship constructs have strengthened over time ( Khamitov et al. 2019 ) and is a silver lining for practitioners and managers who strive to enhance trust.

CHANGE IN EFFECTIVENESS OF ANTECEDENTS OF CONSUMER TRUST ACROSS TIME

p < .001; Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

We report the results for each specific antecedent in web appendix H . Interestingly, we find that marketing investments have grown in importance in recent years (although in a marginally significant way: p = .098), which is a testament to the continued effects of the positive signals that marketing mix instruments convey ( Rajavi et al. 2019 ).

Target of Trust

Target of trust plays an important role when it comes to the relative influence of antecedents on consumer trust ( table 3 ). We focus primarily on big-picture differences in (magnitude of) effects of drivers of trust by comparing average IBTA versus RBTA effects. Though on average there is no significant difference in the strength of effects of IBTA versus RBTA for specific offerings and technologies, IBTA are significantly more effective in driving trust toward brands/firms and industries as compared to RBTA. Being intangible entities, brands are increasingly viewed by many consumers as a series of normatively binding expectations that are ethically akin to brand promises ( Bhargava and Bedi 2022 ) and are expected to be honest and well-intentioned relational agents ( Khamitov et al. 2019 ), making it easier to drive trust by conveying integrity. As for industries, because a number of industries (fuel and energy, banking, aviation, tobacco, and alcohol) over the years have left consumers with the impression that some industries lack integrity ( Darke and Ritchie 2007 ), if and when a certain industry can convince consumers of its moral uprightness, such efforts are particularly effective in driving trust.

SPLIT-SAMPLE ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF TRUST ENTITY

p < .001; ψ: number of effect sizes. For average IBTA and RBTA effects, we focused on absolute value of effect sizes. Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Out of 983 effect sizes for antecedents, we were not able to categorize 134 of them into any of the above four categories (e.g., target of trust was an employee). A full table with estimates for each antecedent is presented in web appendix H .

Comparing average IBTA and RBTA effects across different entities is also worthwhile. While there is no significant difference in IBTA effects across brands/firms and specific offerings (all pairwise p -values >.10), IBTA are significantly stronger (weaker) in driving trust toward industries (technologies). This implies a particularly strong role for industry integrity (aligned with the discussion above), which is unlike the relatively weaker technology benevolence mandate. Also, while RBTA are similarly effective in driving trust toward brands/firms and technologies (all pairwise p -values >.10), they are stronger (weaker) in driving trust toward specific offerings (industries). We conjecture that unlike with other trust entities, consumers’ responses to specific product/service offerings are influenced more heavily by an offering’s perceived practical and functional reliability in meeting their requirements.

Type of Attribute

We performed the type of attribute moderator analyses by comparing IBTA and RBTA meta-analytic coefficients for not-search versus search, not-experience versus experience, and not-credence versus credence attributes. We provide the results in table 4 . There is only one statistically significant difference: the magnitude of the effectiveness of IBTA is significantly stronger for non-experience attributes than for experience attributes ( p = .006). That is, if quality or other characteristics remain unknown until consumption (i.e., experience attributes), whether a good has higher or lower integrity is unlikely be diagnostic when it comes to trusting the good.

ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF ATTRIBUTE

p < .001; because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

Put differently, if the consumer can evaluate a good only by way of experience, communicating integrity and ethicality may not be that meaningful for trust-building ( Grabner-Kraeuter 2002 ).

Consequences of Consumer Trust (RQ3)

When we compare aggregated PAC with aggregated primarily behavioral ones in table 5 , we observe a stronger magnitude of effect for attitudinal consequences ( b PAC = 0.431, SE = 0.010 vs. b PBC = 0.353, SE = 0.015, p < .001), which makes sense because behavioral outcomes are further down the purchase funnel and might be strongly affected by other variables (e.g., price, availability, etc.), hence lowering the overall importance of trust in driving them. This finding reinforces the hierarchy of effects and attitude-behavior gap theories ( Barry and Howard 1990 ). When it comes to individual consequences of trust, the most notable results are for satisfaction ( b = 0.494, SE = 0.027, p < .001; top consequence) and attitudinal loyalty ( b = 0.404, SE = 0.014, p < .001; third strongest consequence), which are in line with the classic tripartite relationship quality theory ( Connors et al. 2021 ; Fletcher, Simpson, and Thomas 2000 ).

RESULTS FOR CONSEQUENCES OF CONSUMER TRUST

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, the magnitude of coefficient estimates is directly comparable across the consequences of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the five attitudinal-based (the four behavioral-based) consequences to construct the aggregate variables. The estimates for the covariates and the deviance values are based on models with the nine consequences included.

Trust remains the most important currency in lasting relationships … . In times of turbulence and volatility, trust is what holds society together. (Edelman “Trust Barometer” 2021)

Theoretical and Practical Contributions

Closing the consumer trust gap.

Over the last five decades, numerous articles from various disciplines have expanded our understanding of the individual consumer’s trust. Although the extant research demonstrates the crucial role played by consumer trust, no consensus has been reached regarding which antecedents and consequences of the individual consumer’s trust are most powerful. Furthermore, a vast majority of such studies employ a singular focus, context, operationalization, and/or sample and, hence, have been unable to examine conditions under which antecedents of consumer trust become more rather than less effective. The present research is the first to systematically investigate the antecedents and consequences of consumer trust, as well as important moderators across a very broad body of multidisciplinary work, and to shed light on the differential strength of these antecedents and consequences. In so doing, we advance the extant literatures on both consumer trust ( Chaudhuri and Holbrook 2001 ; Darke and Ritchie 2007 ; Engeler and Barasz 2021 ; Sirdeshmukh et al. 2002 ) and empirical generalizations in consumer research ( Khamitov et al. 2019 ; Weingarten and Goodman 2021 ).

Integrity Over Reliability

From a practical standpoint, the empirical generalizations distilled by the current research can and should be used as managerial benchmarks when it comes to driving and benefiting from consumer trust. For instance, managers are encouraged to prioritize establishing integrity over conveying reliability, to strategically prioritize top drivers of consumer trust (e.g., reputation, ethicality and SR, perceived quality, attachment), and to allocate resources accordingly. Such an approach is warranted, as businesses typically have limited resources, which is why effective trust-building approaches are critical. To this end, in web appendix H , we also provide granular trust-driver results that can be used by managers in charge of a brand/firm (ethicality/SR, reputation, attachment), specific offering (competence, attachment, perceived value), industry (reputation, ethicality/SR, perceived quality), or technology (perceived value, reputation, perceived quality).

Strong Effect of Consumer Trust on Attitudinal and Behavioral Outcomes

On the surface, consumer trust is logically expected to lead to strong market performance. However, lack of systematic and generalizable evidence on the exact nature of benefits associated with consumer trust has led some experts to draw on anecdotal evidence and undermine the importance of fostering consumer trust ( Marketing Week 2021 ). Our findings stand in contrast to such claims and highlight the strong effect of consumer trust on desirable outcomes. Not only does consumer trust result in enhanced attitudinal consequences of satisfaction, attitudinal loyalty, self-concept connection, evaluations, and engagement, but it also boosts behavioral consequences like purchase intentions, behavioral loyalty, willingness to pay, and even market performance.

Consumer Trust in the Future

The increasing importance of the right antecedent levers.

The cross-time findings, alongside recent industry reports regarding change in baseline trust yield interesting insights. While reports by Edelman (2021) , Gallup (2023) , and Millward Brown (2018) suggest that baseline consumer trust has declined, our findings imply that all is not doom and gloom, and that managerial actions now have more power to move the needle and improve consumers’ trust. In other words, although, in general, many consumers have lost trust in brands, brands can more easily make up for that loss in baseline trust by engaging in the right activities (conveying integrity via a reputation campaign or CSR, increasing the quality of their offerings).

The Nuanced Impact on Downstream Consequences Over Time

How has the importance of consumer trust in driving outcomes changed? Both researchers and practitioners would benefit greatly from insights regarding the future influence of consumer trust on different outcomes. To speak to the future role of trust, we conducted additional exploratory analyses on consequences of consumer trust by comparing meta-analytic coefficients in recent versus older studies. We likewise conjectured that the change in trends in older versus more recent studies would be manifested in the future: the outcomes that trust more strongly affects in recent studies will be impacted by it strongly in the future as well. On the aggregate, we do not find significant evidence for change in the effectiveness of consumer trust in driving PAC and PBC. However, when looking at individual consequences, we find that in recent years, the effect of consumer trust on behavioral loyalty and market performance has strongly increased. Interestingly, and contrary to the claims made by some practitioners ( Marketing Week 2021 ), trust has recently become (and will most likely continue to be) more important in driving consumer purchase decisions. Additionally, the effect of consumer trust in enhancing behavioral loyalty has also increased in recent years. We present the detailed results in web appendix H .

Implications and Future Research Agenda

Probing integrity further.

The current article opens avenues for further research. First, the impressively strong impact of reputation, ethicality, and SR on consumer trust speaks to the effectiveness of inherently moral precursors of generating trust and the importance of doing the right thing . These integrity antecedents emerged the strongest among a number of contenders. Thus, scholars are encouraged to pay increased attention to studying various nuances related to how and why reputational and moral considerations influence consumer trust as well as studying the apparent importance of establishing integrity over establishing reliability in the marketplace (which is particularly meaningful amid the growing proliferation of unsuccessful sociopolitical activism efforts, greenwashing, and CSI). Related to this, past research has shown that when it comes to choosing between service providers, consumers prioritize competent ones over moral ones ( Kirmani et al. 2017 ). Our findings paint a different picture when it comes to consumer trust. Future research on tradeoffs between consumers’ trust and choice across settings is needed.

Rethinking Certain Antecedents

Second, the relatively low average capacity of perceived risk and marketing investments to influence trust is interesting and rather surprising, implying that their effects on trust are likely to be weaker than previously thought. This former finding is different than Geyskens et al.’s (1998) finding regarding the importance of risk and uncertainty in driving trust in the B2B context. This might be because the individual consumer is less calculative than the organizational customer. In this connection, future research should investigate conditions under which risk and marketing investments hold the ground and serve as more effective drivers of the individual consumer’s trust (e.g., types or magnitude of risk and marketing investments).

Digging Deeper into the Moderators

Further, the finding that different trust entities have differential effectiveness of their respective antecedents implies that there is likely no one-size-fits-all approach to driving consumer trust. That is, depending on which target trust is directed at (brands/firms vs. specific offerings vs. industries vs. technologies), the impact of different antecedents varies quite dramatically. This is consistent with the idea of the increasingly nuanced marketplace wherein nowadays consumers have to put trust in both humans and machines, whereas humans were more of the focus in the past. Therefore, future researchers must carefully select a particular trust entity context of interest and avoid expecting uniform effects. Depending on the context, consumer trust scholars should be able to calibrate their expectations and shortlist a handful of manipulations holding the highest potential when it comes to predicting trust (e.g., manipulating competence to drive trust in crowdfunding requestors; Wang et al. 2021 ).

Interestingly, looking at temporal patterns and trajectories within our meta-analytic data for recent versus older years as well as attribute type differences spurs a number of research pathways. These trends naturally prompt the following questions: Why do we observe such increases and decreases, respectively? What are some of the factors driving this evolution over time and this IBTA effectiveness gap for experience attributes? Can scholars expect the same patterns moving forward? Future work is urged in this regard, and we explicitly call for research identifying certain conditions where trust is still highly impactful on consumer outcomes.

Consumer Trust in a Post-Truth World

Importantly, one can argue that consumers are increasingly distrustful of media in general and of social media in particular, especially in the United States with the prevalence of fake news and one’s inability to distinguish truth from lies in these contexts. Is it likely that this distrust finds its way into a general distrust of products and brands? Has time come to determine more latent ways in which trust might affect consumers’ decisions even when they do not explicitly state it as important? Relatedly, would the increasing levels of nationalism being observed across the globe lead to a distrust of foreign brands?

Calling for Greater Ecological Validity

Lastly, a fairly strong trust-market performance link warrants elaboration. On the one hand, this effect is reassuring, as it implies that the positive substantial effects of trust are not limited to attitudes and behavioral intentions. On the other hand, only a handful of included studies (i.e., 28 effect sizes) focused on market performance. Against this background, more studies of ecologically valid downstream financial and market consequences are urgently needed going forward because of their (1) superior representation of the real-world marketplace, (2) current lower sample size, and (3) higher potential to arrive at realistic, non-inflated effect sizes.

Examining Understudied Constructs

To keep the scope of our work manageable, following other meta studies we focused on the most prevalent antecedents of our focal construct. However, many other antecedents of consumer trust have been discussed in past research. A few examples are propensity to trust ( Yamagishi and Yamagishi 1994 ), warmth ( Kirmani et al. 2017 ), and familiarity ( Garbarino and Johnson 1999 ). Future research could look through other theoretical lenses and meta-analyze another set of understudied antecedents not examined in our research.

Trust Dimensionality

The dimensionality of trust warrants further investigation. Our review of past research indicates that trust is predominantly conceptualized as two-dimensional (65% of papers, web appendix I ), aligning with our findings regarding the differential effects of IBTA versus RBTA. The most commonly studied dimensions are reliability and integrity, although other dimensions such as sympathy and familiarity are also mentioned in the literature, albeit rarely ( web appendix I ). While we adopted a two-dimensional conceptualization of trust, given the limited available data in prior papers, our empirical modeling treated trust as a unidimensional construct with two groups of antecedents inspired by the most commonly examined dimensions of trust. Future work could explore the relationships between antecedents and different dimensions of trust in greater detail.

The collection and coding of data for the meta-analysis were administered at Indiana University and Georgia Institute of Technology between Fall of 2020 and Summer of 2023. The first two authors designed the coding protocol and conducted data analyses. The third and fourth authors carried out data collection under supervision of the first two authors. Data and coding were discussed on multiple occasions by all authors. The final article was jointly authored. The data are currently stored in a project directory on the Open Science Framework.

Mansur Khamitov ( [email protected] ) is an assistant professor of marketing at the Kelley School of Business, Indiana University, 1309 E 10th St, Bloomington, IN 47405, USA.

Koushyar Rajavi ( [email protected] ) is an assistant professor of marketing at the Scheller College of Business, Georgia Institute of Technology, 800 W Peachtree St NW, Atlanta, GA 30308, USA.

Der-Wei Huang ( [email protected] ) is an assistant professor of marketing at the School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) 518172, China.

Yuly Hong ( [email protected] ) is an assistant professor of marketing at NEOMA Business School, 59 rue Pierre Taittinger, 51100 Reims, France.

All authors contributed equally. The authors are grateful for funding from RATS Grant (2236360/MKHAM) to Mansur Khamitov provided by the Kelley School of Business, Indiana University. Supplementary materials are included in the web appendix accompanying the online version of this article.

We acknowledge that there have been meta-analytic studies on the role of consumer trust in specific contexts (e.g., Kim and Peterson’s study (2017) of the role of online trust in e-commerce); yet such studies are context-specific, smaller in scale, and their conclusions might not be generalizable to consumer trust in other settings or across settings. We also acknowledge Khamitov, Wang, and Thomson’s study (2019) meta-analyzing the link between brand relationships and customer loyalty that (1) focuses only on trust towards a single, specific entity (brand), (2) does not study any brand-trust antecedents, (3) explores a single brand-trust consequence (customer brand loyalty), and (4) includes fewer effect sizes (216).

As can be seen in our discussion of prior literature, different labels have been used to refer to similar and/or closely related components of trust (e.g., reliability, capability, ability). We acknowledge that there might be slight conceptual differences between these constructs. We utilize the integrity versus reliability dichotomy, which, in our view, most succinctly and parsimoniously represents the literature on consumer trust across different domains.

It should be noted that some perspectives on trust place more emphasis on the inherent characteristics of the trusting entity (e.g., propensity to trust in the literature on individual trust). In the current work, following a large body of research on consumer trust, we view trust as a temporary state experienced by consumers when they examine brands, products, services, etc., rather than a stable personality trait. This perspective is pertinent to practitioners, for it focuses on antecedents that business entities can modify to enhance consumer trust. Thus, ** we do not examine factors that are related to the stable nature of trust that practitioners have little or no influence over (e.g., consumers’ general propensity to trust).

On the basis of these two criteria, for instance, we excluded perceived warmth (appeared in <2% of the past studies on consumer trust) and familiarity/experience (lack of fit with our theoretical framework).

Using the 10% threshold led to fewer consequences compared to antecedents (seven vs. eight). To have more balance between the number of antecedents and consequences, and to provide more insights with respect to different marketplace outcomes tied to trust, we also included the next two commonly studied consequence variables: market performance and willingness to pay.

Our assignment of certain antecedents to IBTA versus RBTA is based on the primary mechanism in the literature. Our framework is not meant to suggest that a variable categorized as IBTA (RBTA) has no impact at all on the reliability (integrity) trust aspect.

To further justify the categorization of trust consequences/outcomes as primarily attitudinal versus primarily behavioral, we refer the reader to past research like Chaudhuri and Holbrook (2001) , Boonlertvanich (2019) , or Liu et al. (2021) where this attitudinal versus behavioral distinction is apparent and central.

We also caution that our framework does not suggest that trust would never drive any of our antecedents, such as attachment and/or reputation. To assign a construct to antecedents or consequences of consumer trust, we relied on past research and determined its role in the nomological framework based on the majority of the past research. Resultantly, our antecedents and consequences were used in the same role in more than 80% of past research. As such, our focal relationship specification between constructs represents a better-fitting depiction of the extant literature (and not a universal depiction).

Of the overall sample, 983 effect sizes from 347 studies across 310 manuscripts correspond to antecedents of trust, while 1,164 effect sizes from 459 studies across 414 manuscripts capture consequences of trust.

A full list of included papers is available at https://researchbox.org/1335&PEER_REVIEW_passcode=YPKQTP .

Our follow-up interaction analysis based on low versus high level of financial risk suggests that risk does not influence trust in the low-risk subset ( b = −0.101, p = .186), whereas in the high financial risk subset, the effect of risk is substantial ( b = −0.592, p < .001). Relatedly, while in the low physical risk subset the impact of risk on trust is b = −0.102 ( p = .176), the influence of risk on trust is much stronger under high physical risk ( b = −0.355, p = .038).

We present pairwise significance tests across coefficients of antecedents (and consequences) in web appendix G .

The 2015 year of publication threshold leads to a good balance of effect sizes for recent and older studies, as well as allowing us to focus specifically on the most recent studies that are pertinent to understanding what the future might look like.

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Abstract: The success of employment is highly related to a job seeker's capability of communicating and collaborating with others. While leveraging one's network during the job-seeking process is intuitive to the neurotypical, this can be challenging for people with autism. Recent empirical findings have started to show how facilitating collaboration between people with autism and their social surroundings through new design can improve their chances of employment. This work aims to provide actionable guidelines and conceptual frameworks that future researchers and practitioners can apply to improve collaborative design for job-seekers with autism. Built upon the literature on past technological interventions built for supporting job-seekers with autism, we define three major research challenges of (1) communication support, (2) employment stage-wise support, and (3) group work support. For each challenge, we review the current state-of-the-art practices and possible future solutions. We then suggest future designs that can provide breakthroughs from the interdisciplinary lens of human-AI collaboration, health services, group work, accessibility computing, and natural language processing.

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An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping

• Original Paper
• Published: 16 May 2024

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• Zhiqiang Zhang 1 , 2 , 3 ,
• Gongwen Wang   ORCID: orcid.org/0000-0002-0141-7209 4 ,
• Emmanuel John M. Carranza 5 ,
• Jingguo Du 6 ,
• Yingjie Li 1 , 2 , 3 ,
• Xinxing Liu 1 , 2 , 3 &
• Yongjun Su 7  

The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, as well as 3D geological modeling process, and (c) epistemic uncertainty due to predictive algorithm modeling. Quantifying the uncertainty of 3D MPM is a prerequisite for accepting predictive models in exploration. Previous MPM studies were centered on addressing the mineral system conceptual model uncertainty. To the best of our knowledge, few studies quantified the aleatoric and epistemic uncertainties of 3D MPM. This study proposes a novel uncertainty-quantification machine learning framework to qualify aleatoric and epistemic uncertainties in 3D MPM by the uncertainty-quantification random forest. Another innovation of this framework is utility of the accuracy–rejection curve to provide a quantitative uncertainty threshold for exploration target delineation. The Bayesian hyperparameter optimization tunes the hyperparameters of the uncertainty-quantification random forest automatically. The case study of 3D MPM for exploration target delineation in the Wulong gold district of China demonstrated the practicality of our framework. The aleatoric uncertainty of the 3D MPM indicates that the 3D Early Cretaceous dyke model is the main source of this uncertainty. The 3D exploration targets delineated by the uncertainty-quantification machine learning framework can benefit subsurface gold exploration in the study area.

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Acknowledgments

This research is supported by Hebei Natural Science Foundation (No. D2023403051), Open Project Program of Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research, Hebei GEO University, China (No. HGUXT-2023-13), and the MNR Key Laboratory for Exploration Theory & Technology of Critical Mineral Resources (No. 202405).

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Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

Zhiqiang Zhang, Yingjie Li & Xinxing Liu

Hebei Key Laboratory of Strategic Critical Mineral Resources, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

School of Earth Sciences, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

MNR Key Laboratory for Exploration Theory and Technology of Critical Mineral Resources, China University of Geosciences, Beijing, 100083, People’s Republic of China

Gongwen Wang

Geological of Geology, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa

Emmanuel John M. Carranza

School of Earth Science and Resources, Chang’An University, Xi’an, 710054, People’s Republic of China

Tianjin Center, China Geological Survey, Tianjin, 300170, People’s Republic of China

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Zhang, Z., Wang, G., Carranza, E.J.M. et al. An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping. Nat Resour Res (2024). https://doi.org/10.1007/s11053-024-10349-x

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Received : 12 January 2024

Accepted : 13 April 2024

Published : 16 May 2024

DOI : https://doi.org/10.1007/s11053-024-10349-x

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• Aleatoric uncertainty
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