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What is a Conceptual Framework and How to Make It (with Examples)

A strong conceptual framework underpins good research. A conceptual framework in research is used to understand a research problem and guide the development and analysis of the research. It serves as a roadmap to conceptualize and structure the work by providing an outline that connects different ideas, concepts, and theories within the field of study. A conceptual framework pictorially or verbally depicts presumed relationships among the study variables.

The purpose of a conceptual framework is to serve as a scheme for organizing and categorizing knowledge and thereby help researchers in developing theories and hypotheses and conducting empirical studies.

In this post, we explain what is a conceptual framework, and provide expert advice on how to make a conceptual framework, along with conceptual framework examples.

Table of Contents

What is a Conceptual Framework in Research

Definition of a conceptual framework.

A conceptual framework includes key concepts, variables, relationships, and assumptions that guide the academic inquiry. It establishes the theoretical underpinnings and provides a lens through which researchers can analyze and interpret data. A conceptual framework draws upon existing theories, models, or established bodies of knowledge to provide a structure for understanding the research problem. It defines the scope of research, identifying relevant variables, establishing research questions, and guiding the selection of appropriate methodologies and data analysis techniques.

Conceptual frameworks can be written or visual. Other types of conceptual framework representations might be taxonomic (verbal description categorizing phenomena into classes without showing relationships between classes) or mathematical descriptions (expression of phenomena in the form of mathematical equations).

Figure 1: Definition of a conceptual framework explained diagrammatically

Conceptual Framework Origin

The term conceptual framework appears to have originated in philosophy and systems theory, being used for the first time in the 1930s by the philosopher Alfred North Whitehead. He bridged the theological, social, and physical sciences by providing a common conceptual framework. The use of the conceptual framework began early in accountancy and can be traced back to publications by William A. Paton and John B. Canning in the first quarter of the 20 th century. Thus, in the original framework, financial issues were addressed, such as useful features, basic elements, and variables needed to prepare financial statements. Nevertheless, a conceptual framework approach should be considered when starting your research journey in any field, from finance to social sciences to applied sciences.

Purpose and Importance of a Conceptual Framework in Research

The importance of a conceptual framework in research cannot be understated, irrespective of the field of study. It is important for the following reasons:

• It clarifies the context of the study.
• It justifies the study to the reader.
• It helps you check your own understanding of the problem and the need for the study.
• It illustrates the expected relationship between the variables and defines the objectives for the research.
• It helps further refine the study objectives and choose the methods appropriate to meet them.

What to Include in a Conceptual Framework

Essential elements that a conceptual framework should include are as follows:

• Overarching research question(s)
• Study parameters
• Study variables
• Potential relationships between those variables.

The sources for these elements of a conceptual framework are literature, theory, and experience or prior knowledge.

How to Make a Conceptual Framework

Now that you know the essential elements, your next question will be how to make a conceptual framework.

For this, start by identifying the most suitable set of questions that your research aims to answer. Next, categorize the various variables. Finally, perform a rigorous analysis of the collected data and compile the final results to establish connections between the variables.

In short, the steps are as follows:

• Choose appropriate research questions.
• Define the different types of variables involved.
• Determine the cause-and-effect relationships.

Be sure to make use of arrows and lines to depict the presence or absence of correlational linkages among the variables.

Developing a Conceptual Framework

Researchers should be adept at developing a conceptual framework. Here are the steps for developing a conceptual framework:

1. Identify a research question

Your research question guides your entire study, making it imperative to invest time and effort in formulating a question that aligns with your research goals and contributes to the existing body of knowledge. This step involves the following:

• Choose a broad topic of interest
• Conduct background research
• Narrow down the focus
• Define your goals
• Make it specific and answerable
• Consider significance and novelty
• Seek feedback.

 2. Choose independent and dependent variables

The dependent variable is the main outcome you want to measure, explain, or predict in your study. It should be a variable that can be observed, measured, or assessed quantitatively or qualitatively. Independent variables are the factors or variables that may influence, explain, or predict changes in the dependent variable.

Choose independent and dependent variables for your study according to the research objectives, the nature of the phenomenon being studied, and the specific research design. The identification of variables is rooted in existing literature, theories, or your own observations.

3. Consider cause-and-effect relationships

To better understand and communicate the relationships between variables in your study, cause-and-effect relationships need to be visualized. This can be done by using path diagrams, cause-and-effect matrices, time series plots, scatter plots, bar charts, or heatmaps.

4. Identify other influencing variables

Besides the independent and dependent variables, researchers must understand and consider the following types of variables:

• Moderating variable: A variable that influences the strength or direction of the relationship between an independent variable and a dependent variable.
• Mediating variable: A variable that explains the relationship between an independent variable and a dependent variable and clarifies how the independent variable affects the dependent variable.
• Control variable: A variable that is kept constant or controlled to avoid the influence of other factors that may affect the relationship between the independent and dependent variables.
• Confounding variable: A type of unmeasured variable that is related to both the independent and dependent variables.

Example of a Conceptual Framework

Let us examine the following conceptual framework example. Let’s say your research topic is “ The Impact of Social Media Usage on Academic Performance among College Students .” Here, you want to investigate how social media usage affects academic performance in college students. Social media usage (encompassing frequency of social media use, time spent on social media platforms, and types of social media platforms used) is the independent variable, and academic performance (covering grades, exam scores, and class attendance) is the dependent variable.

This conceptual framework example also includes a mediating variable, study habits, which may explain how social media usage affects academic performance. Study habits (time spent studying, study environment, and use of study aids or resources) can act as a mechanism through which social media usage influences academic outcomes. Additionally, a moderating variable, self-discipline (level of self-control and self-regulation, ability to manage distractions, and prioritization skills), is included to examine how individual differences in self-control and discipline may influence the relationship between social media usage and academic performance.

Confounding variables are also identified (socioeconomic status, prior academic achievement), which are potential factors that may influence both social media usage and academic performance. These variables need to be considered and controlled in the study to ensure that any observed effects are specifically attributed to social media usage. A visual representation of this conceptual framework example is seen in Figure 2.

Figure 2: Visual representation of a conceptual framework for the topic “The Impact of Social Media Usage on Academic Performance among College Students”

Key Takeaways

Here is a snapshot of the basics of a conceptual framework in research:

• A conceptual framework is an idea or model representing the subject or phenomena you intend to study.
• It is primarily a researcher’s perception of the research problem. It can be used to develop hypotheses or testable research questions.
• It provides a preliminary understanding of the factors at play, their interrelationships, and the underlying reasons.
• It guides your research by aiding in the formulation of meaningful research questions, selection of appropriate methods, and identification of potential challenges to the validity of your findings.
• It provides a structure for organizing and understanding data.
• It allows you to chalk out the relationships between concepts and variables to understand them.
• Variables besides dependent and independent variables (moderating, mediating, control, and confounding variables) must be considered when developing a conceptual framework.

Frequently Asked Questions

What is the difference between a moderating variable and a mediating variable.

Moderating and mediating variables are easily confused. A moderating variable affects the direction and strength of this relationship, whereas a mediating explains how two variables relate.

What is the difference between independent variables, dependent variables, and confounding variables?

Independent variables are the variables manipulated to affect the outcome of an experiment (e.g., the dose of a fat-loss drug administered to rats). Dependent variables are variables being measured or observed in an experiment (e.g., changes in rat body weight as a result of the drug). A confounding variable distorts or masks the effects of the variables being studied because it is associated both with dependent variable and with the independent variable. For instance, in this example, pre-existing metabolic dysfunction in some rats could interact differently with the drug being studied and also affect rat body weight.

Should I have more than one dependent or independent variable in a study?

The need for more than one dependent or independent variable in a study depends on the research question, study design, and relationships being investigated. Note the following when making this decision for your research:

• If your research question involves exploring the relationships between multiple variables or factors, it may be appropriate to have more than one dependent or independent variable.
• If you have specific hypotheses about the relationships between several variables, it may be necessary to include multiple dependent or independent variables.
• Adequate resources, sample size, and data collection methods should be considered when determining the number of dependent and independent variables to include.

What is a confounding variable?

A confounding variable is not the main focus of the study but can unintentionally influence the relationship between the independent and dependent variables. Confounding variables can introduce bias and give rise to misleading conclusions. These variables must be controlled to ensure that any observed relationship is genuinely due to the independent variable.

What is a control variable?

A control variable is something not of interest to the study’s objectives but is kept constant because it could influence the outcomes. Control variables can help prevent research biases and allow for a more accurate assessment of the relationship between the independent and dependent variables. Examples are (i) testing all participants at the same time (e.g., in the morning) to minimize the potential effects of circadian rhythms, (ii) ensuring that instruments are calibrated consistently before each measurement to minimize the influence of measurement errors, and (iii) randomization of participants across study groups.

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

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.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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What is a good example of a conceptual framework?

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A well-designed study doesn’t just happen. Researchers work hard to ensure the studies they conduct will be scientifically valid and will advance understanding in their field.

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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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• Methodology
• 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

• Allee, V. (2000). Knowledge networks and communities of learning . OD Practitioner , 32 ( 4 ), 4–13. [ Google Scholar ]
• Allen, M. (2017). The Sage encyclopedia of communication research methods (Vols. 1–4 ). Los Angeles, CA: Sage. 10.4135/9781483381411 [ CrossRef ] [ Google Scholar ]
• American Association for the Advancement of Science. (2011). Vision and change in undergraduate biology education: A call to action . Washington, DC. [ Google Scholar ]
• Anfara, V. A., Mertz, N. T. (2014). Setting the stage . In Anfara, V. A., Mertz, N. T. (eds.), Theoretical frameworks in qualitative research (pp. 1–22). Sage. [ Google Scholar ]
• Barnes, M. E., Brownell, S. E. (2016). Practices and perspectives of college instructors on addressing religious beliefs when teaching evolution . CBE—Life Sciences Education , 15 ( 2 ), ar18. https://doi.org/10.1187/cbe.15-11-0243 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Boote, D. N., Beile, P. (2005). Scholars before researchers: On the centrality of the dissertation literature review in research preparation . Educational Researcher , 34 ( 6 ), 3–15. 10.3102/0013189x034006003 [ CrossRef ] [ Google Scholar ]
• Booth, A., Sutton, A., Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago, IL: University of Chicago Press. [ Google Scholar ]
• Brownell, S. E., Kloser, M. J. (2015). Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology . Studies in Higher Education , 40 ( 3 ), 525–544. https://doi.org/10.1080/03075079.2015.1004234 [ Google Scholar ]
• Connolly, M. R., Lee, Y. G., Savoy, J. N. (2018). The effects of doctoral teaching development on early-career STEM scholars’ college teaching self-efficacy . CBE—Life Sciences Education , 17 ( 1 ), ar14. https://doi.org/10.1187/cbe.17-02-0039 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cooper, K. M., Blattman, J. N., Hendrix, T., Brownell, S. E. (2019). The impact of broadly relevant novel discoveries on student project ownership in a traditional lab course turned CURE . CBE—Life Sciences Education , 18 ( 4 ), ar57. https://doi.org/10.1187/cbe.19-06-0113 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• DeHaan, R. L. (2011). Education research in the biological sciences: A nine decade review (Paper commissioned by the NAS/NRC Committee on the Status, Contributions, and Future Directions of Discipline Based Education Research) . Washington, DC: National Academies Press. Retrieved May 20, 2022, from www7.nationalacademies.org/bose/DBER_Mee ting2_commissioned_papers_page.html [ Google Scholar ]
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research . Physical Review Physics Education Research , 15 ( 2 ), 020101. [ Google Scholar ]
• Dirks, C. (2011). The current status and future direction of biology education research . Paper presented at: Second Committee Meeting on the Status, Contributions, and Future Directions of Discipline-Based Education Research, 18–19 October (Washington, DC). Retrieved May 20, 2022, from http://sites.nationalacademies.org/DBASSE/BOSE/DBASSE_071087 [ Google Scholar ]
• Duran, R. P., Eisenhart, M. A., Erickson, F. D., Grant, C. A., Green, J. L., Hedges, L. V., Schneider, B. L. (2006). Standards for reporting on empirical social science research in AERA publications: American Educational Research Association . Educational Researcher , 35 ( 6 ), 33–40. [ Google Scholar ]
• Ebert-May, D., Derting, T. L., Henkel, T. P., Middlemis Maher, J., Momsen, J. L., Arnold, B., Passmore, H. A. (2015). Breaking the cycle: Future faculty begin teaching with learner-centered strategies after professional development . CBE—Life Sciences Education , 14 ( 2 ), ar22. https://doi.org/10.1187/cbe.14-12-0222 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Galvan, J. L., Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). New York, NY: Routledge. https://doi.org/10.4324/9781315229386 [ Google Scholar ]
• Gehrke, S., Kezar, A. (2017). The roles of STEM faculty communities of practice in institutional and departmental reform in higher education . American Educational Research Journal , 54 ( 5 ), 803–833. https://doi.org/10.3102/0002831217706736 [ Google Scholar ]
• Ghee, M., Keels, M., Collins, D., Neal-Spence, C., Baker, E. (2016). Fine-tuning summer research programs to promote underrepresented students’ persistence in the STEM pathway . CBE—Life Sciences Education , 15 ( 3 ), ar28. https://doi.org/10.1187/cbe.16-01-0046 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Institute of Education Sciences & National Science Foundation. (2013). Common guidelines for education research and development . Retrieved May 20, 2022, from www.nsf.gov/pubs/2013/nsf13126/nsf13126.pdf
• Jensen, J. L., Lawson, A. (2011). Effects of collaborative group composition and inquiry instruction on reasoning gains and achievement in undergraduate biology . CBE—Life Sciences Education , 10 ( 1 ), 64–73. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kolpikova, E. P., Chen, D. C., Doherty, J. H. (2019). Does the format of preclass reading quizzes matter? An evaluation of traditional and gamified, adaptive preclass reading quizzes . CBE—Life Sciences Education , 18 ( 4 ), ar52. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Labov, J. B., Reid, A. H., Yamamoto, K. R. (2010). Integrated biology and undergraduate science education: A new biology education for the twenty-first century? CBE—Life Sciences Education , 9 ( 1 ), 10–16. https://doi.org/10.1187/cbe.09-12-0092 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lane, T. B. (2016). Beyond academic and social integration: Understanding the impact of a STEM enrichment program on the retention and degree attainment of underrepresented students . CBE—Life Sciences Education , 15 ( 3 ), ar39. https://doi.org/10.1187/cbe.16-01-0070 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life . New York, NY: Cambridge University Press. [ Google Scholar ]
• Lo, S. M., Gardner, G. E., Reid, J., Napoleon-Fanis, V., Carroll, P., Smith, E., Sato, B. K. (2019). Prevailing questions and methodologies in biology education research: A longitudinal analysis of research in CBE — Life Sciences Education and at the Society for the Advancement of Biology Education Research . CBE—Life Sciences Education , 18 ( 1 ), ar9. https://doi.org/10.1187/cbe.18-08-0164 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lysaght, Z. (2011). Epistemological and paradigmatic ecumenism in “Pasteur’s quadrant:” Tales from doctoral research . In Official Conference Proceedings of the Third Asian Conference on Education in Osaka, Japan . Retrieved May 20, 2022, from http://iafor.org/ace2011_offprint/ACE2011_offprint_0254.pdf
• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• Miles, M. B., Huberman, A. M., Saldaña, J. (2014). Qualitative data analysis (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• 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 [ Google Scholar ]
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Los Angeles, CA: Sage. [ Google Scholar ]
• Perry, J., Meir, E., Herron, J. C., Maruca, S., Stal, D. (2008). Evaluating two approaches to helping college students understand evolutionary trees through diagramming tasks . CBE—Life Sciences Education , 7 ( 2 ), 193–201. https://doi.org/10.1187/cbe.07-01-0007 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Posner, G. J., Strike, K. A., Hewson, P. W., Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change . Science Education , 66 ( 2 ), 211–227. [ Google Scholar ]
• Ravitch, S. M., Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. [ Google Scholar ]
• Reeves, T. D., Marbach-Ad, G., Miller, K. R., Ridgway, J., Gardner, G. E., Schussler, E. E., Wischusen, E. W. (2016). A conceptual framework for graduate teaching assistant professional development evaluation and research . CBE—Life Sciences Education , 15 ( 2 ), es2. https://doi.org/10.1187/cbe.15-10-0225 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reynolds, J. A., Thaiss, C., Katkin, W., Thompson, R. J. Jr. (2012). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , 11 ( 1 ), 17–25. https://doi.org/10.1187/cbe.11-08-0064 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rocco, T. S., Plakhotnik, M. S. (2009). Literature reviews, conceptual frameworks, and theoretical frameworks: Terms, functions, and distinctions . Human Resource Development Review , 8 ( 1 ), 120–130. https://doi.org/10.1177/1534484309332617 [ Google Scholar ]
• Rodrigo-Peiris, T., Xiang, L., Cassone, V. M. (2018). A low-intensity, hybrid design between a “traditional” and a “course-based” research experience yields positive outcomes for science undergraduate freshmen and shows potential for large-scale application . CBE—Life Sciences Education , 17 ( 4 ), ar53. https://doi.org/10.1187/cbe.17-11-0248 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sabel, J. L., Dauer, J. T., Forbes, C. T. (2017). Introductory biology students’ use of enhanced answer keys and reflection questions to engage in metacognition and enhance understanding . CBE—Life Sciences Education , 16 ( 3 ), ar40. https://doi.org/10.1187/cbe.16-10-0298 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sbeglia, G. C., Goodridge, J. A., Gordon, L. H., Nehm, R. H. (2021). Are faculty changing? How reform frameworks, sampling intensities, and instrument measures impact inferences about student-centered teaching practices . CBE—Life Sciences Education , 20 ( 3 ), ar39. https://doi.org/10.1187/cbe.20-11-0259 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schwandt, T. A. (2000). Three epistemological stances for qualitative inquiry: Interpretivism, hermeneutics, and social constructionism . In Denzin, N. K., Lincoln, Y. S. (Eds.), Handbook of qualitative research (2nd ed., pp. 189–213). Los Angeles, CA: Sage. [ Google Scholar ]
• Sickel, A. J., Friedrichsen, P. (2013). Examining the evolution education literature with a focus on teachers: Major findings, goals for teacher preparation, and directions for future research . Evolution: Education and Outreach , 6 ( 1 ), 23. https://doi.org/10.1186/1936-6434-6-23 [ Google Scholar ]
• Singer, S. R., Nielsen, N. R., Schweingruber, H. A. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering . Washington, DC: National Academies Press. [ Google Scholar ]
• Todd, A., Romine, W. L., Correa-Menendez, J. (2019). Modeling the transition from a phenotypic to genotypic conceptualization of genetics in a university-level introductory biology context . Research in Science Education , 49 ( 2 ), 569–589. https://doi.org/10.1007/s11165-017-9626-2 [ Google Scholar ]
• Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Cambridge, MA: Harvard University Press. [ Google Scholar ]
• Wenger, E. (1998). Communities of practice: Learning as a social system . Systems Thinker , 9 ( 5 ), 2–3. [ Google Scholar ]
• Ziadie, M. A., Andrews, T. C. (2018). Moving evolution education forward: A systematic analysis of literature to identify gaps in collective knowledge for teaching . CBE—Life Sciences Education , 17 ( 1 ), ar11. https://doi.org/10.1187/cbe.17-08-0190 [ PMC free article ] [ PubMed ] [ Google Scholar ]

Research Process Guide

• Step 1 - Identifying and Developing a Topic
• Step 2 - Narrowing Your Topic
• Step 3 - Developing Research Questions
• Step 4 - Conducting a Literature Review
• Step 5 - Choosing a Conceptual or Theoretical Framework
• Step 6 - Determining Research Methodology
• Step 6a - Determining Research Methodology - Quantitative Research Methods
• Step 6b - Determining Research Methodology - Qualitative Design
• Step 7 - Considering Ethical Issues in Research with Human Subjects - Institutional Review Board (IRB)
• Step 8 - Collecting Data
• Step 9 - Analyzing Data
• Step 10 - Interpreting Results
• Step 11 - Writing Up Results

Step 5: Choosing a Conceptual or Theoretical Framework

For all empirical research, you must choose a conceptual or theoretical framework to “frame” or “ground” your study. Theoretical and/or conceptual frameworks are often difficult to understand and challenging to choose which is the right one (s) for your research objective (Hatch, 2002). Truthfully, it is difficult to get a real understanding of what these frameworks are and how you are supposed to find what works for your study. The discussion of your framework is addressed in your Chapter 1, the introduction and then is further explored through in-depth discussion in your Chapter 2 literature review.

“Theory is supposed to help researchers of any persuasion clarify what they are up to and to help them to explain to others what they are up to” (Walcott, 1995, p. 189, as cited in Fallon, 2016). It is important to discuss in the beginning to help researchers “clarify what they are up to” and important at the writing stage to “help explain to others what they are up to” (Fallon, 2016).  

What is the difference between the conceptual and the theoretical framework?

Often, the terms theoretical framework and conceptual framework are used interchangeably, which, in this author’s opinion, makes an already difficult to understand idea even more confusing. According to Imenda (2014) and Mensah et al. (2020), there is a very distinct difference between conceptual and theoretical frameworks, not only how they are defined but also, how and when they are used in empirical research.

Imenda (2014) contends that the framework “is the soul of every research project” (p.185). Essentially, it determines how the researcher formulates the research problem, goes about investigating the problem, and what meaning or significance the research lends to the data collected and analyzed investigating the problem.  

Very generally, you would use a theoretical framework if you were conducting deductive research as you test a theory or theories. “A theoretical framework comprises the theories expressed by experts in the field into which you plan to research, which you draw upon to provide a theoretical coat hanger for your data analysis and interpretation of results” (Kivunja, 2018, p.45 ).  Often this framework is based on established theories like, the Set Theory, evolution, the theory of matter or similar pre-existing generalizations like Newton’s law of motion (Imenda, 2014). A good theoretical framework should be linked to, and possibly emerge from your literature review.

Using a theoretical framework allows you to (Kivunja, 2018):

• Increase the credibility and validity of your research
• Interpret meaning found in data collection
• Evaluate solutions for solving your research problem

According to Mensah et al.(2020) the theoretical framework for your research is not a summary of your own thoughts about your research. Rather, it is a compilation of the thoughts of giants in your field, as they relate to your proposed research, as you understand those theories, and how you will use those theories to understand the data collected.

Additionally, Jabareen (2009) defines a conceptual framework as interlinked concepts that together provide a comprehensive  understanding of a phenomenon. “A conceptual framework is the total, logical orientation and associations of anything and everything that forms the underlying thinking, structures, plans and practices and implementation of your entire research project” (Kivunja, 2018, p. 45). You would largely use a conceptual framework when conducting inductive research, as it helps the researcher answer questions that are core to qualitative research, such as the nature of reality, the way things are and how things really work in a real world (Guba & Lincoln, 1994).

Some consideration of the following questions can help define your conceptual framework (Kinvunja, 2018):

• What do you want to do in your research? And why do you want to do it?
• How do you plan to do it?
• What meaning will you make of the data?
• Which worldview will you situate your study in? (i.e. Positivist? Interpretist? Constructivist?)

Examples of conceptual frameworks include the definitions a sociologist uses to describe a culture and the types of data an economist considers when evaluating a country’s industry. The conceptual framework consists of the ideas that are used to define research and evaluate data. Conceptual frameworks are often laid out at the beginning of a paper or an experiment description for a reader to understand the methods used (Mensah et al., 2020).

Writing it up

After choosing your framework is to articulate the theory or concept that grounds your study by defining it and demonstrating the rationale for this particular set of theories or concepts guiding your inquiry.  Write up your theoretical perspective sections for your research plan following your choice of worldview/ research paradigm. For a quantitative study you are particularly interested in theory using the procedures for a causal analysis. For qualitative research, you should locate qualitative journal articles that use a priori theory (knowledge that is acquired not through experience) that is modified during the process of research (Creswell & Creswell, 2018). Also, you should generate or develop a theory at the end of your study. For a mixed methods study which uses a transformative (critical theoretical lens) identify how the lens specifically shapes the research process.                                   

Creswell, J. W., & Creswell, J. D. (2 018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Fallon, M. (2016). Writing up quantitative research in the social and behavioral sciences. Sense. https://kean.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&AuthType=cookie,ip,url,cpid&custid=keaninf&db=nlebk&AN=1288374&site=ehost-live&scope=site&ebv=EB&ppid=pp_C1

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of Qualitative Research, 2 (163-194), 105.

Hatch, J. A. ( 2002). Doing qualitative research in education settings. SUNY Press.

Imenda, S. (2014). Is there a conceptual difference between theoretical and conceptual frameworks?  Journal of Social Sciences, 38 (2), 185-195.

Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8 (4), 49-62.

Kivunja, C. ( 2018, December 3). Distinguishing between theory, theoretical framework, and conceptual framework. The International Journal of Higher Education, 7 (6), 44-53. https://files.eric.ed.gov/fulltext/EJ1198682.pdf  

Mensah, R. O., Agyemang, F., Acquah, A., Babah, P. A., & Dontoh, J. (2020). Discourses on conceptual and theoretical frameworks in research: Meaning and implications for researchers. Journal of African Interdisciplinary Studies, 4 (5), 53-64.

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Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

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

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

Conceptual Framework in Research

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

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

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

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

Conceptual Model

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

Empirical Framework

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

Descriptive Framework

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

Analytical Framework

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

Conceptual Framework for Policy Analysis

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

Logical Frameworks

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

Conceptual Frameworks for Program Evaluation

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

Conceptual Frameworks for Organizational Analysis

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

Conceptual Frameworks for Strategic Planning

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

Components of Conceptual Framework

The components of a conceptual framework typically include:

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

Conceptual Framework Methodology

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

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

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

Conduct a Literature Review

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

Define key Concepts and Variables

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

Develop a Theoretical Framework

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

Test the Framework

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

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

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

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

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

Purpose of Conceptual Framework

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

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

When to use Conceptual Framework

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

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

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

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

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

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

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

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

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Conceptual Framework and Research Design

• First Online: 01 January 2013

Cite this chapter

• Clive-Steven Curran 2  

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After the practical relevance of an anticipation of areas of convergence has been underlined in the last chapter, the following paragraphs will discuss the theoretical reasoning for it.

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It is estimated that digestive/gut health is the largest sector of NFF, followed by the heart health market with a wide selection of products aiming especially at lowering the risk of cholesterol-related cardiovascular diseases [cf. 1 ].

For a more detailed discussion of NFF as a setting of convergence see especially [ 2 , pp. 131ff.; 3 ]. For further literature on NFF see Sect. 4.3 .

For a detailed discussion of different theories’ possible contribution in explaining strategic actions in the light of convergence (and especially market convergence), see [ 4 , pp. 35ff.].

It is still being discussed whether the resource-based view should actually be considered (and called) the RBT. As RBV is the more widely used expression, it will be used in the study at hand. For a detailed discussion regarding RBV/RBT see, e.g., [ 5 , pp. 11ff.]. See also Acedo, Barroso and Galan, who delimit the general framework of the RBT from the main trends: RBV, KBV (knowledge-based view), and the relational view [ 6 , p. 621].

In this article, the authors provide a detailed overview of the RBV’s possible weaknesses and critiques. In summary, they draw the conclusion that the RBV can withstand five of the eight discussed critiques quite well, while the explanations on resource, value, and competitive advantage should be widened in future developments of this theory. In any case, the ‘ultimate challenge’ to the RBV lies in its conclusions being proven to be right or wrong for larger ‘populations of firms’ and not so much in the verification of single constructs [cf. 9 , p. 530; 10 , p. 1142].

He actually sees the building of special values and a distinctive competence as a prime function of leadership [cf. 11 , p. 27].

It is not intended in the present study to provide a comprehensive introduction to the RBV. In fact, this section is meant to briefly introduce the RBV as a theory to explain firms’ actions in a setting of convergence and as a foundation for the present study’s research questions laid out in Sect. 4.2 . For more in-depth discussions of the RBV see, e.g., [ 5 , 6 , 8 , 14 – 17 ].

Although firms are expected to exhibit greater similarity regarding their resource profile in intra-industry than in inter-industry settings cf., e.g., [ 2 , p. 94; 18 , p. 83].

A resource’s value will most likely lie in reducing costs or increasing the value of a product or process to customers; competitors will experience difficulties in trying to substitute it with another resource or to imitate it; and it will be too rare to be equally sourced by competitors [cf. 10 , p. 1142].

For a review of different definitions of resources and authors’ use of them see, e.g., [ 17 , pp. 463ff.].

While this study mainly uses the term ‘resources’, the term ‘assets’ will be used interchangeably, where appropriate. This has also been done by, e.g., [ 2 , 18 , 21 , 22 ].

Naturally, not all of these assets are of strategic importance.

zu Knyphausen-Aufseß also delineates both theories from each other, stressing the general outside perspective of the organization.

While knowledge may often refer to hardly explicable knowledge or trade secrets, DeCarolis and Deeds view patents as “representative of stocks of organizational knowledge” [cf. 7 , p. 958].

In contrast to this study and other cited works, they actually view ‘component competence’ as including resources and not as a capability to deploy resources. Thus, in the remainder of the text ‘competence’ will be replaced by ‘capabilities’ to account for this difference.

Generally, due to tight regulations in respect to pharmaceutical drugs, the development process is highly structured into different stages of discovery and pre-clinical trials as well as four “phases” during clinical trials. As most of the actions within drug development need to be registered with the authorities, this process can be more easily monitored than in most other industries [cf., e.g., 26 , pp. 205f].

[cf. 19 , p. 115], for a discussion of reputation as a source for competitive advantage. According to his line of reasoning, reputation (which is a large part of a brand name’s value) will only serve as a resource, if not all firms have good reputations and they are thus rare and inimitable.

See, e.g., [ 2 , pp. 137f], for an overview of competency differences between the food and the pharmaceutical industries. Of course, the pharmaceutical industry also puts a lot of effort into marketing its products, especially when selling (or competing with) so-called “generic drugs” (generics). Generics are pharmaceutical drugs that are largely ‘imitations’ of “original drugs”, after expiry of their patent protection. As generics manufacturers are facing substantially lower development costs, these generics sell at considerably lower prices. This leads to an increased competition between all the producers of drugs with the respective indication and mostly to increased marketing efforts.

See also [ 7 , p. 954].

According to the reasoning of Yeoh and Roth, internal R&D efforts are more efficient and in the long run also more productive, as economies of experience can be acquired. This argument appears reasonable for well established therapeutic areas, but less convincing against a background of drying product pipelines and a tendency of large pharmaceutical companies to gain access to new knowledge and new drug candidates by acquisition of especially smaller biotech companies. In respect to therapeutic market focus, they highlight the importance of such a focus on single therapeutic markets, as understanding of complex diseases and treatments as well as the respective markets makes shifting from one to the other (or adding a new one) a rather costly adventure.

Drug approval success is seen as mirroring a company’s accumulated R&D competence, which lead to an approved drug in more cases than in a less successful company. Generally, the probability of any substance reaching approval as a pharmaceutical drug is about 0.01 %. (Relative) emphasis on radical innovation refers to more effort put into developing new compound entities rather than modifying or combining existing products.

Who conclude their study with the assessment that the “pharmaceutical industry requires firm strategies that capitalize on resources and capabilities” [ 20 , p. 649].

A firm’s knowledge stock is deemed to be critical for a sustained competitive advantage, especially in dynamic environments [cf. 7 , p. 965].

See also [ 7 , p. 964].

Acknowledging the fact, that terms like ‘good performance’ or ‘success’ are rather fuzzy and have been defined and especially operationalized very differently in literature, they are only used for illustrative purposes. No specific definition and operationalization is therefore provided here.

When core competencies become weaknesses they are sometimes called core rigidities cf., e.g., [ 29 , p. 28].

In contrast to the RBV with its proven strong empirical support, managers should not “invest heavily in guidance grounded in a theoretical perspective that has only modest support” [ 10 , p. 1151].

This better assessment does, of course, not have to be grounded on an in-depth rational analysis. While it may as well be the result of a ‘lucky guess’, firms should strive to employ methods ridding them of coincidental factors.

The 1973/1974 oil crisis was marked by exploding costs for oil and oil-derived fuels as a reaction to rationing by the affected governments. This was necessary as crude oil supplies were cut back by an embargo of the Arabian OPEC (Organization of the Petroleum Exporting Countries) members to the USA, Japan, and several European countries. Caused by opposition to the (anticipated) US and European support of Israel in the Yom Kippur War, it is interpreted as a first massive manifestation of ‘the oil weapon’. Most scholars attribute this embargo not only to a political reasoning, but also the target to increase earnings created by oil exports and to shift bargaining power from buyers to sellers of oil. Besides the broad political effects, the oil crisis also led to a major global economic crisis in the following. For an in-depth analysis of the triggers, drivers, and effects of the oil crisis see, e.g., [ 30 , 31 ].

This is particularly the case, where companies will collectively engage in industrial organizations to agree on industry standards, but also to influence public opinion as well as individual politicians and governments.

See, e.g., customers’ reactions to Apple’s various iPad and iPhone models. Many consumers have pre-ordered them without ever having seen it (other than on pictures and videos). Of course, this was partly due to fascination with the product and its functionalities. But unarguably, peoples’ trust in Apple and the functionalities of its products also played a considerable role. Similar reactions to brands can be observed prior to the introduction of new models of some exclusive luxury car makers.

This refers mainly to scientific publications and patent documents as proxies for different steps of the convergence process. It is decided by two factors. First, only if science convergence can at all be tracked in scientific publications, they can also be used to assess the degree of convergence on different levels. Second, if scientific publications are to serve as a precursor of developments in technologies and eventually industries, science convergence must be traceable in them earlier than in patent documents.

It will be part of the results and discussion sections to answer the question whether one converged industry of cosmetics, food, and pharmaceuticals is likely to be formed at the intersection of these three distinct industries.

What exactly constitutes a ‘developed country’ is a considerably contentious issue in a longstanding debate. Within this study, it is used to describe countries that are generally regarded as having a highly industrialized economy and a very high standard of living. Ample definitions and indicators are employed by many organizations. One largely accepted is the human development index (HDI) used by the United Nations Development Programme and comprised proxies for measuring long and healthy lives, access to knowledge, and a decent standard of living. In the 2009 version, 38 countries are listed as having a very high human development status (HDI ranks 1–38), including, e.g., most of Europe; Australia and New Zealand; Hong Kong, Japan, Republic of Korea and Singapore; Israel, Kuwait and the United Arab Emirates; Canada and the USA [cf. 35 ]. In the context of food and eating habits, other countries would have to be included in so far as parts of their population were leading a lifestyle typical for developed countries. See the remainder of this paragraph for a further short explanation.

This definition is a modification of the FUFOSE definition to be found in an article attributed to Diplock et al. [ 42 , p. S6].

Notwithstanding good reasons for including such food products into a definition of NFF, their existence is not a result of innovative activities caused by drivers of convergence. Even if their marketing would be altered (which is often being done in respect to health beneficial natural ingredients) to highlight the physiological effects, these activities could not be observed on the basis of an anticipation approach. Such shifts in marketing foci would hardly be reported in any of the suggested data sources, with the likely exception of companies’ websites or similar marketing channels.

Accentuation according to original document.

This is done mainly because the lines between conventional foods and Functional Foods as well as between Nutraceuticals and pharmaceutical drugs are similarly fine. Accordingly, an exclusion of Nutraceuticals from the further analyses could be justifiably challenged as being basically arbitrary. Furthermore, there is no reason to believe that fading boundaries between the food and the pharmaceutical industries would per se favor the appearance of either of the two product groups.

See also [ 45 , p. 62].

See Sect. 4.3.3.3 for the concept and use of ‘health claims’ in NFF.

Efficacy is a term mainly used in the pharmaceutical sciences. Efficacy is tested in respect to the question whether a treatment does more good than harm when delivered under optimum conditions. In contrast to that, effectiveness is tested under real-world conditions. Consequently, efficacy is necessary but not sufficient to achieve effectiveness [cf. 46 , p. 451; see also 47 , pp. 1261ff.].

The lack of effectiveness based on typical diets is one major criticism in respect to NFF, even where authors accept the validity of a claimed proof of efficacy.

The largely preventive nature of NFF is a widely accepted difference to pharmaceutical drugs. While they may as well have the target to provide future benefits, many drugs are used for an immediate effect. While this immediate effect may be achieved by consumption of NFF as well (see, e.g., the description of phytosterols in Sect. 4.3.3 ), it is almost always targeted at the prevention of future adverse health effects [cf. 2 , p. 144; 48 , p. 408].

According to Leatherhead Food International, consumer sales in the USA are comprised about 40/35/25 % heart/gut/bone-related NFF.

A particularly detailed list of functions is provided in Ref. [ 49 , p. S410]. And 24 different functions are listed to be considered under Chinese food regulation: immune regulation, postponement of senility, memory improvement, promotion of growth and development, anti-fatigue, body weight reduction, oxygen deficit tolerance, radiation protection, anti-mutation, anti-tumor, blood lipid regulation, improvement of sexual potency, blood glucose regulation, gastrointestinal function improvement, sleep improvement, improvement of nutritional anemia, protection of liver from chemical damage, lactation improvement, improvement for beauty, vision improvement, promotion of lead removal, removal of ‘intense heat’ from the throat and moistening of the throat, blood pressure regulation, and enhancement of bone calcification.

These differences will also be briefly discussed in Sect. 4.3.4 .

Many of the aspects considering individual substance classes and their use in NFF are similar when considering different classes; hence a discussion of one class may serve well as an example for most of the classes. Furthermore, phytosterols were chosen because of their use as an example in the remainder of the study. See particularly Sect. 5.1 for the results on convergence anticipation in the area of phytosterols and Sects. 6.1.1 , 6.1.2 , 6.1.3 for a discussion of these results. For a more comprehensive coverage of different substances in NFF, their commercial viability as well as regulatory activities and public perception cf., e.g., [ 36 , 51 , 53 ]. For a comprehensive analysis of phytosterols in respect of chemical, biological, medical, regulatory, and marketing aspects see also [ 54 ].

For a more detailed description of biological functions of phytosterols see, e.g., [cf. 56 , p. 939, 57 – 61 ].

In addition to different classification hierarchies of phytosterols and phytostanols, nomenclature of phytosterols is particularly confusing as researchers and companies follow international attempts at standardization only in part. Two main streams are following the IUPAC-IUB [IUPAC = International Union of Pure and Applied Chemistry; IUB = International Union of Biochemistry, now the International Union of Biochemistry and Molecular Biology (IUBMB)] recommendations of 1976 and 1989, which are numbering the carbon atoms of the C-24 functional group as C-28, 29, and C24 1 , 24², respectively. This study will instead use the common (trivial) names, as is mainly done by regulators and companies as well [cf. 56 , p. 460].

In plant tissues, phytosterols occur in these four forms: as the free alcohol, fatty acids, steryl glycosides, and acylated steryl glycosides. While the hydroxyl group at C-3 is underivatized in the free alcohol, it is covalently bound to other constituents in the three latter phytosterol conjugates [cf. 56 , pp. 465ff.]. They also report on differences in phytosterol forms in individual species and tissues.

In contrast, bananas, apples and tomatoes, for example, only contain 160, 120, and 70 mg/kg edible portion, respectively. Furthermore, see [ 57 , p. 947], who also compare crude oil contents with up to 15.57 and 32.25 g/kg phytosterol content in crude corn oil and rice bran oil, respectively. Unsurprisingly, phytosterol concentrations vary greatly with different production processes and steps. Phytosterols are also commonly derived from tall oil, a by-product in the production of wood pulp from coniferous trees. For a list of commercial suppliers of phytosterols and their sourcing from tall oil or vegetable oil, see [ 66 , pp. 3ff].

Piironen et al. provide a comprehensive overview of phytosterol contents in various foods.

According to Calpe-Berdiel et al., these three phytosterols represent more than 95 % of total phytosterol dietary intake [cf. 63 , p. 19].

[cf. 56 , pp. 465ff] for a more detailed description of phytosterol composition in different species and organs.

For a comprehensive overview of phytosterol stereochemistry see, e.g., [ 56 ].

Ostlund reports on a greater variance in the estimated daily intake, ranging from 167 to 437 mg [cf. 65 , p. 537]. Even higher values are provided by Poli et al. for people in Mediterranean countries (500–600 mg/d) [cf. 67 , p. S10].

LDL = low density lipoprotein. LDLs are the parts of IDLs (intermediate density lipoproteins) not taken up by the liver and serve as the most important carriers of cholesterol in the blood. These IDLs are formed from VLDLs (very low density lipoproteins), which serve as a transporter of unnecessary cholesterol and triacylglycerine from the liver into the blood. In contrast to LDLs, which are carrying cholesterol to peripheral tissues and regulate the de - novo synthesis of cholesterol there, HDLs (high density lipoproteins) take up cholesterol from dead cells and transport it back to the liver or transfer it to VLDLs or LDLs [cf. 68 , pp. 800f].

For a more precise description of the molecular actions of phytosterols in cholesterol metabolism and a review of recent studies, see [ 63 ].

For Europe, the British Heart Foundation has estimated cardiovascular diseases to cause costs of approximately €110 billion in 2006, nearly 10 % of total healthcare expenditures, underlining the medical and economic importance of potential improvements to heart health [cf. 69 , p. 21].

Statins block the enzyme HMG-CoA reductase (3-hydroxy-3-methylglutaryl-coenzyme A reductase or HMGR), the rate-controlling enzyme in the metabolic pathway producing cholesterol in the liver. [cf. 68 , pp. 804f] These statins include different active pharmaceutical ingredients such as rosuvastatin, lovastatin, and atorvastatin. Atorvastatin is marketed by Pfizer under the brand name LIPITOR®, constituting the best-selling prescription pharmaceutical product in the world (2009 sales at US$ 11.4 billion), despite lower sales than in previous years (US$ 12.4 billion in 2008 and 12.7 billion in 2007) [cf. 71 , p. 21].

See Sect. 4.3.3.3 for a discussion of phytosterol use in NFF.

In their study reduction amounted to 14.1 % within 12 months of 2.6 g/d sitostanol ester consumed.

For a comprehensive discussion of the different studies on ‘health-promoting effects of phytosterols’ and their subtypes see, e.g., [ 56 , 57 , 77 ].

For a further discussion of this finding see also [ 78 ].

It would go beyond the scope of this study to discuss in length the different positive and negative aspects of each individual substance. In addition to efficacy, bioavailability, and ease of preparation other factors such as raw material availability and prices would have to be taken into account as well. See, e.g., [ 56 ] For example, absorption and resulting availability of phytosterols is one central point in efficacy discussions already since 1959 [cf. 79 ].

Poli et al. also stress the fact that dosage in excess of about 2.5 g/d does not provide additional benefits [cf. 67 , p. S11]. This significant reduction is underlined by other studies investigating the effects of lowered cholesterol levels on heart disease prevention. For instance, Law et al. found that a long-term reduction in serum concentration of 10 % led to a lowering of the risk of ischemic heart disease of between 20 and 50 % [cf. 80 , p. 367].

The original quotations are two identical sentences with either sterol or stanol. Based on the data available at the time of the ruling, the FDA required these health claims to recommend a daily intake of 3.4 g stanyl esters and only 1.3 g of steryl esters (or the respective amounts in free stanols/sterols) [cf. 77 , p. 54712]. According to Moreau et al. this was caused by the on average higher dosages administered in stanol studies compared to such with sterols [cf. 56 , p. 488].

However, they also stress the limitations of their study in respect to generalizability due to patient sample composition.

For potential anticancerogenic effects see also, e.g., [ 82 – 84 ].

This inherited sterol storage disease with a strong predisposition to premature coronary atherosclerosis is very rare. In fact, research showed a reduction of serum sterol levels under administration of the unabsorbable sitostanol, possibly by competitive inhibition of sterol absorption [cf. 85 , pp. 181ff.]. For other potentially affected patients see also [ 86 ].

Natural vitamin E includes actually eight distinct molecules: α -, β -, γ -, and δ -tocopherol as well as α -, β -, γ -, and δ -tocotrienol [cf. 87 , p. 692]. However, only α -tocopherol meets human vitamin E requirements and is thus often referred to as ‘the’ vitamin E [cf. 88 , p. 5].

Antioxidants may protect cells in the human bodies against free radicals that stem from, e.g., the breakdown of consumed food, smoke, or radiation and are believed to play a role in heart disease, cancer, and other diseases [cf. 89 ].

Slightly different findings are reported by Chen et al. who find qualitatively comparable but larger deviations in tocopherol and carotene levels [cf. 81 , pp. 277ff.].

Moreau et al. also mention the possibility that free sterols and stanols could have a lesser adverse effect on the absorption of antioxidants.

Quotation translated by the author. They are also used in low concentrations (e.g., thrice daily 20 mg) pharmaceutical drugs like, e.g., in Germany Schwarz Pharma’s Harzol®, Triastonal® and Sitosterin Prostata-Kapseln or Sandoz’ Azuprostat Sandoz® (twice daily 65 mg) for the treatment of benign prostatic hyperplasia [cf. 97 ].

Starling covers the introduction of Right Direction Foods’ Right Direction Cookies , which did interestingly not carry the FDA approved health claim due to trans fat and sugar content.

See Sect. 4.3.4.2 for a brief overview of regulation of NFF in Japan, the USA and the EU.

In other countries, like the USA, the Benecol brand is marketed by McNeil Nutritionals (part of Johnson & Johnson) [cf. 101 ].

Although EFSA did also voice a positive opinion in regard to Danone’s Danacol products, these are still awaiting a decision by the European Commission. EFSA is in the process of reviewing in total more than 175 claims in relation to cholesterol and CHD.

Apparently, it does in fact not make a difference whether phytosterols are consumed in the form of Nutraceuticals or FF, or as pharmaceutical drugs. Hence, it would be up to the consumer to decide on the most convenient (and economical) way of securing a daily dose of phytosterols.

Yoghurts (drinks) have experienced considerably higher growth rates due to the elevated interest by consumers [cf. 105 ].

cf., e.g., [ 107 , 108 – 111 ]. See also Sect. 4.3.5 for a short excursus on Cosmeceuticals.

However, market size and growth rates are reasonably disputed. A different estimate by Frost and Sullivan sees the market in 2007 at €420 million, growing at 20 % per year [cf. 114 , 115 ]. According to [ 1 ], Leatherhead Food Research even forecasted a 10 % decline in the UK cholesterol-lowering spread market in 2009 after an allegedly similar development in 2008, despite overall growth in the FF market. His line of reasoning is built on the existence of contradictory messages from governments, companies, medical authorities, and the media as well as several competing products, like spreads with polyunsaturated or omega-3 fatty acids. Two further estimates see the global market for end-products containing sterol-based esters at €500 million in 2005 and the global market for phytosterol-based end products at US$ 805 million (US$ 600 million for Europe, US$ 130 million for Japan and US$ 75 million for the USA) [cf. 99 , 116 ].

Schaffnit-Chatterjee estimates the global market size for processed foods (constituting about 75 % of total world food sales) in 2009 to have reached US$ 3 trillion [ 120 , p. 13].

The quotation is by Pam Stauffer, global communication manager at Cargill Health, and Food Technologies [cf. 124 ].

PLM is used to distinguish this monitoring for signals of unexpected health effects from post-marketing surveillance (PMS), a much more rigorous system used in the pharmaceutical industry [cf. 128 , p. 1214].

For a thorough assessment of consumer demographics and rationale see also [ 100 , pp. 18ff.].

It is not the aim of this study to provide a comprehensive assessment of regulatory aspects of the NFF sector. Instead, this section is intended to provide a brief introduction to the importance of national and international regulation and its importance in the development of markets for NFF. For deeper insights into regulatory aspects see, e.g., [ 2 , pp. 147ff.; 36 , pp. 115ff.; 51 , pp. 55f.; 133 , pp. 1ff.; 134 , pp. 9ff.].

The Codex Alimentarius and its standards, guidelines, and related texts have been compiled by the Codex Alimentarius Commission, created jointly by Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO) in 1963. Its main targets are “protecting health of the consumers and ensuring fair trade practices in the food trade, and promoting coordination of all food standards work undertaken by international governmental and non-governmental organizations” [cf. 135 ].

While EU regulations are applied in all Member States without the necessity for any further implementation, directives are only setting objectives to be achieved by means deemed sensible by the respective Member State. Both are commonly adjusted by amendments or corrections.

(EC) 258/97, Article 1(2.).

Phytostanol esters as one example for a novel food already on the market have been discussed in the preceding chapter.

(EC) 1924/2006, Article 3.

cf. (EC) 1924/2006, Article 2(2.).

Dureja et al. also provide a brief overview of common cosmeceutical contents. A further overview of types of cosmeceutical agents can be found in, e.g., [ 143 , 144 ].

See, e.g., Crompton 139 who also cites Nicholas Perricone, founder of NV Perricone MD Cosmeceuticals, opposing accusations of Cosmeceuticals’ low credibility: “Cosmeceuticals are science-based rather than marketing-driven, giving the customer results and value for their investment”.

This document constitutes the consolidated version of the original Council Directive 76/768/EEC and all its amendments and corrections up to mid2008 See also [ 142 , p. 1148].

Ref. [ 146 ]. While this advertisement is for one specific product, most other advertisements will sound similar, irrespective of company or country.

[cf. 151 ]. For a more thorough description of the regulation system in the USA see also [ 140 ].

For an overview of the personal care industry and the most important companies see, e.g., [ 154 ].

A different estimate by Kline is US$ 57 billion globally in 2008, with Europe, the USA and Japan accounting for 35, 18 and 11 %, respectively [cf. 155 ].

Palmer, D.: Functional foods facing an uncertain future. Australian Food News (2009)

Google Scholar  

Bröring, S.: The Front End of Innovation in Converging Industries: The Case of Nutraceuticals and Functional Foods. DUV, Wiesbaden, Germany (2005)

Book   Google Scholar  

Bröring, S., Cloutier, L.M., Leker, J.: The front end of innovation in an era of industry convergence: evidence from nutraceuticals and functional foods. R&D Manage. 36 (5), 487–498 (2006)

Article   Google Scholar  

Stieglitz, N.: Strategie und Wettbewerb in konvergierenden Märkten. Deutscher Universitäts, Wiesbaden, Germany (2004)

Nothnagel, K.: Empirical Research within Resource-Based Theory: A Meta-Analysis of the Central Propositions. Strategisches Kompetenz-Management. Gabler GWV Fachverlage, Wiesbaden, Germany (2008)

Acedo, F.J., Barroso, C., Galan, J.L.: The resource-based theory: dissemination and main trends. Strateg. Manag. J. 27 (7), 621–636 (2006)

DeCarolis, D.M., Deeds, D.L.: The impact of stocks and flows of organizational knowledge on firm performance: an empirical investigation of the biotechnology industry. Strateg. Manag. J. 20 (10), 953–968 (1999)

Kraaijenbrink, J., Spender, J.C., Groen, A.J.: The resource-based view: a review and assessment of its critiques. J. Manage. 36 (1), 349–372 (2010). doi: 10.1177/0149206309350775

Godfrey, P.C., Hill, C.W.L.: The problem of unobservable in strategic management research. Strateg. Manag. J. 16 (7), 519–533 (1995)

Crook, T.R., Ketchen Jr, D.J., Combs, J.G., Todd, S.Y.: Strategic resources and performance: a meta-analysis. Strateg. Manag. J. 29 (11), 1141–1154 (2008)

Selznick, P.: Leadership in Administration: A Sociological Interpretation. University of California Press, Berkeley and Los Angeles (CA), USA (1957)

Penrose, E.T.: The Theory of the Growth of the Firm. Oxford University Press, Oxford (1959)

Wernerfelt, B.: A resource-based view of the firm. Strateg. Manag. J. 5 (2), 171–180 (1984)

Peteraf, M.A.: The cornerstones of competitive advantage: a resource-based view. Strateg. Manag. J. 14 (3), 179–192 (1993)

Teece, D.J., Pisano, G., Shuen, A.: Dynamic capabilities and strategic management. Strateg. Manag. J. 18 (7), 509–533 (1997)

Silverman, B.S.: Technological resources and the direction of corporate diversification: toward an integration of the resource-based view and transaction cost economics. Manage. Sci. 45 (8), 1109–1124 (1999)

Article   MATH   Google Scholar  

zu Knyphausen-Aufseß, D.: Auf dem Weg zu einem ressourcenorientierten Paradigma? Resource Dependence-Theorie der Organisation und Resource-based View des Strategischen Managements im Vergleich. In: Ortmann, G., Sydow, J., Türk, K. (eds.) Theorien der Organisation: Die Rückkehr der Gesellschaft. pp. 452–480. Westdeutscher, Wiesbaden, Germany (2000)

Herzog, P.: Open and Closed Innovation - Different Cultures for Different Strategies. Gabler, Wiesbaden (2008)

Barney, J.: Firm resources and sustained competitive advantage. J. Manage. 17 (1), 99–120 (1991). doi: 10.1177/014920639101700108

Yeoh, P.-L., Roth, K.: An empirical analysis of sustained advantage in the U.S. pharmaceutical industry: impact of firm resources and capabilities. Strateg. Manag. J. 20 (7), 637–653 (1999)

Liebermann, M.B., Montgomery, D.B.: First mover (dis)advantages: retrospective and link with the resource-based view. Strateg. Manag. J. 19 (12), 1111–1125 (1998)

Afuah, A.: Innovation management: Strategies, implementation and profits. Oxford University Press, Oxford, UK (2003)

Amit, R., Schoemaker, P.J.H.: Strategic assets and organizational rent. Strateg. Manag. J. 14 (1), 33–46 (1993)

Henderson, R., Cockburn, I.: Measuring Competence? Exploring Firm Effects in Pharmaceutical Research. Strategic Management Journal 15 (S1), 63-84 (1994)

Nothnagel, K.: Empirical Research Within Resource-Based Theory: Methodological Challenges and a Meta-Analysis of the Central Propositions. Freie Universität, Berlin (2007)

Amir-Aslani, A., Mangematin, V.: The future of drug discovery and development: Shifting emphasis towards personalized medicine. Technol. Forecast. Soc. Chang. 77 (2), 203–217 (2010)

Helfat, C.E.: Know-how and asset complementarity and dynamic capability accumulation: the case of R&D. Strateg. Manag. J. 18 (5), 339–360 (1997)

Pisano, G.P.: knowledge, integration, and the locus of learning: an empirical analysis of process development. Strateg. Manag. J. 15 (S1), 85–100 (1994)

DeCarolis, D.M.: Competencies and imitability in the pharmaceutical industry: an analysis of their relationship with firm performance. J. Manag. 29 (1), 27–50 (2003). doi: 10.1177/014920630302900103

Hohensee, J.: Der erste Ölpreisschock 1973/74: die politischen und gesellschaftlichen Auswirkungen der arabischen Erdölpolitik auf die Bundesrepublik Deutschland und Westeuropa. Historische Mitteilungen, Beihefte 17. Franz Steiner, Stuttgart, Germany (1996)

Merrill, K.R.: The Oil Crisis of 1973–1974: A Brief History with Documents, 1st edn. Bedford/St. Martin’s, New York, USA (2007)

Ansoff, H.I.: Managing strategic surprise by response to weak signals. California Management Review 18 (2), 21–33 (1975)

Ansoff, H.I.: Strategic issue management. Strateg. Manag. J. 1 (2), 131–148 (1980)

Hasler, C.M.: The changing face of functional foods. J. Am. Coll. Nutr. 19 (5), 499S–506S (2000)

MathSciNet   Google Scholar  

United Nations Development Programme: Human Development Report 2009—Overcoming barriers: Human mobility and development. New York, USA (2009)

Chadwick, R., Henson, S., Moseley, B., Koenen, G., Liakopoulos, M., Midden, C., Palou, A., Rechkemmer, G., Schröder, D., von Wright, A.: Functional Foods. Wissenschaftsethik und Technikfolgenbeurteilung, vol. 20. Springer, Berlin/Heidelberg, Germany (2003)

Wildman, R.E.C., Kelley, M.: Nutraceuticals and functional foods. In: Wildman, R.E.C. (ed.) Handbook of Nutraceuticals and Functional Foods, pp. 1–21. CRC Press, Boca Raton (FL), USA (2007)

Newsholme, E., Leech, T.: Functional Biochemistry in Health and Disease. Wiley, Chichester, UK (2010)

Anonymous: Is functional functioning? Dairy Ind. Int. 72 (11), 17 (2007)

Robertfroid, M.B.: Defining functional foods. In: Gibson, G.R., Williams, C.M. (eds.) Functional foods: Concept to product, pp. 9–27. Woodhead Publishing, Cambridge, UK (2000)

Chapter   Google Scholar  

Kiefer, I., Burger, P., Blass, M., Berghofer, E., Hoppichler, F.: Functional Food—Lebensmittel mit Zusatznutzen? Journal für Ernährungsmedizin 4 (2), 10–15 (2002)

Anonymous: scientific concepts of functional foods in europe consensus document. Brit. J. Nutr. 81 (4), S1–S27 (1999). doi: 10.1017/S0007114599000471

Health Canada: Policy Paper of the Therapeutic Products Programme and the Food Directorate from the Health Protection Branch: Nutraceuticals/Functional Foods and Health Claims on Foods. In., pp. 1-29. Ottawa (ON), Canada, (1998)

EUFIC.: Scientific Substantiation: A key ingredient for functional foods and health claims. http://www.eufic.org/article/en/artid/scientific-substantiation-functional-foods-health-claims/ (2003). Accessed 11 Nov 2010

Thompson, A.K., Moughan, P.J.: Innovation in the foods industry: functional foods. Innovation: Manag., Policy Pract. 10 (1), 61–73 (2008)

Flay, B.R.: Efficacy and effectiveness trials (and other phases of research) in the development of health promotion programs. Prev. Med. 15 (5), 451–474 (1986)

Glasgow, R.E., Lichtenstein, E., Marcus, A.C.: Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am. J. Public Health 93 (8), 1261–1267 (2003). doi: 10.2105/ajph.93.8.1261

Clydesdale, F.M.: Science, education, and technology: new frontiers for health. Crit. Rev. Food Sci. Nutr. 38 (5), 397–419 (1998)

Arai, S.: Global view on functional foods: Asian perspectives. Br. J. Nutr. 88 (Supplement 2), S139–S143 (2002)

Sanders, M.E.: Overview of functional foods: emphasis on probiotic bacteria. Int. Dairy J. 8 (5), 341–347 (1998)

Heasman, M., Mellentin, J.: The Functional Foods Revolution: Healthy people, healthy profits?. Earthscan Publications, London, UK (2001)

Prado, F.C., Parada, J.L., Pandey, A., Soccol, C.R.: Trends in non-dairy probiotic beverages. Food Res. Int. 41 (2), 111–123 (2008). doi: 10.1016/j.foodres.2007.10.010

Wildman, R.E.C. (ed.): Handbook of Nutraceuticals and Functional Foods, 2nd edn. CRC Press, Boca Raton (FL), USA (2007)

Dutta, P.C. (ed.): Phytosterols as Functional Food Components and Nutraceuticals. Marcel Dekker, New York, USA (2004)

International Food Information Council Foundation: Functional Foods. In., pp. 1–4. Washington DC, USA. http://www.foodinsight.org/Content/6/functionalfoodsbackgrounder.pdf (2007). Accessed 10 Apr 2010

Moreau, R.A., Whitaker, B.D., Hicks, K.B.: Phytosterols, phytostanols, and their conjugates in foods: structural diversity, quantitative analysis, and health-promoting uses. Prog. Lipid Res. 41 (6), 457–500 (2002)

Piironen, V., Lindsay, D.G., Miettinen, T.A., Toivo, J., Lampi, A.-M.: Plant sterols: biosynthesis, biological function and their importance to human nutrition. J. Sci. Food Agric. 80 (7), 939–966 (2000)

Demel, R.A., De Kruyff, B.: The function of sterols in membranes. Biochimica et Biophysica Acta (BBA)—Reviews on. Biomembranes 457 (2), 109–132 (1976)

Hartmann, M.-A., Benveniste, P.: Plant membrane sterols: Isolation, identification, and biosynthesis. In: Methods in Enzymology, (vol. 148, pp. 632–650). Academic Press, New York (1987)

McKersie, B.D., Thompson, J.E.: Influence of plant sterols on the phase properties of phospholipid bilayers. Plant Physiol. 63 (5), 802–805 (1979). doi: 10.1104/pp.63.5.802

Haughan, P.A., Lenton, J.R., Goad, L.J.: Sterol requirements and paclobutrazol inhibition of a celery cell culture. Phytochemistry 27 (8), 2491–2500 (1988)

Brufau, G., Canela, M.A., Rafecas, M.: Phytosterols: physiologic and metabolic aspects related to cholesterol-lowering properties. Nutr. Res. 28 (4), 217–225 (2008)

Calpe-Berdiel, L., Escolà-Gil, J.C., Blanco-Vaca, F.: New insights into the molecular actions of plant sterols and stanols in cholesterol metabolism. Atherosclerosis 203 (1), 18–31 (2009)

European Food Safety Authority (efsa).: Consumption of Food and Beverages with Added Plant Sterols in the European Union. Efsa J. (2008) 133 , 1–21. Parma, Italy (2008)

Ostlund, R.E.J.: Phytosterols in human nutrition. Annu. Rev. Nutr. 22 , 533–549 (2002)

Cantrill, R.: Phytosterols, phytostanols and their esters (Chemical and Technical Assessment). In., pp. 1–13. Joint FAO/WHO Expert Committee on Food Additives (JEFCA) (2008)

Poli, A., Marangoni, F., Paoletti, R., Mannarino, E., Lupattelli, G., Notarbartolo, A., Aureli, P., Bernini, F., Cicero, A., Gaddi, A., Catapano, A., Cricelli, C., Gattone, M., Marrocco, W., Porrini, M., Stella, R., Vanotti, A., Volpe, M., Volpe, R., Cannella, C., Pinto, A., Del Toma, E., La Vecchia, C., Tavani, A., Manzato, E., Riccardi, G., Sirtori, C., Zambon, A.: Non-pharmacological control of plasma cholesterol levels. Nutr., Metab. Cardiovas. Dis. 18 (2), S1–S16 (2008)

Berg, J.M., Tymoczko, J.L., Stryer, L.: Biochemie, 5th edn. Spektrum Akademischer, Berlin, Germany (2003)

Anonymous: Cholesterol buster. research*eu 57 (July 2008), 21 (2008)

Moruisi, K.G., Oosthuizen, W., Opperman, A.M.: Phytosterols/stanols lower cholesterol concentrations in familial hypercholesterolemic subjects: a systematic review with meta-analysis. J. Am. Coll. Nutr. 25 (1), 41–48 (2006)

Pfizer Inc.: 2009 Financial Report. New York, USA (2010)

Peterson, D.W.: Effect of soybean sterols in the diet on plasma and liver cholesterol in chicks. Proc. Soc. Exp. Biol. Med. 78 (1), 143–147 (1951)

Pollak, O.J.: Successful prevention of experimental hypercholesteremia and cholesterol atherosclerosis in the rabbit. Circulation 7 (5), 696–701 (1953)

Pollak, O.J.: Reduction of blood cholesterol in man. Circulation 7 (5), 702–706 (1953)

Jones, P.J.H.: Ingestion of phytosterols is not potentially hazardous. J. Nutr. 137 (11), 2485 (2007)

Miettinen, T.A., Puska, P., Gylling, H., Vanhanen, H., Vartiainen, E.: Reduction of serum cholesterol with sitostanol-ester margarine in a mildly hypercholesterolemic population. N. Engl. J. Med. 333 (20), 1308–1312 (1995). doi: 10.1056/nejm199511163332002

Anonymous: Food labeling: health claims; plant sterol/stanol esters and coronary heart disease; Interim Final Rule. In: Department of Health and Human Services: Food and Drug Administration (ed.), vols. 65, 175, pp. 54685–54739. Office of the Federal Register, Washington (DC), USA, (2000)

Plat, J., Kerckhoffs, D.A.J.M., Mensink, R.P.: Therapeutic potential of plant sterols and stanols. Curr. Opin. Lipidol. 11 (6), 571–576 (2000)

Schön, H.: Sterol-balance experiments in humans. Nature 184 (4702), 1872–1873 (1959)

Law, M.R., Wald, N.J., Thompson, S.G.: By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? Br. Med. J. 308 (6925), 367–372 (1994)

Chen, S., Judd, J., Kramer, M., Meijer, G., Clevidence, B., Baer, D.: Phytosterol intake and dietary fat reduction are independent and additive in their ability to reduce plasma LDL cholesterol. Lipids 44 (3), 273–281 (2009)

National Cancer Institute: Phytosterol.: http://www.cancer.gov/Templates/db_alpha.aspx?CdrID=44485 (2008). Accessed 18 April 2008

Woyengo, T.A., Ramprasath, V.R., Jones, P.J.H.: Anticancer effects of phytosterols. Eur. J. Clin. Nutr. 63 , 813–820 (2009)

Jones, P.J.H.a., AbuMweis, S.S.b.: Phytosterols as functional food ingredients: linkages to cardiovascular disease and cancer. Curr. Opin. Clin. Nutr. Metab. Care 12 (2), 147–151 (2009)

Lütjohann, D., von Bergmann, K.: Phytosterolaemia: diagnosis, characterization and therapeutical approaches. Ann. Med. 29 (3), 181–184 (1997)

Patel, M.D., Thompson, P.D.: Phytosterols and vascular disease. Atherosclerosis 186 (1), 12–19 (2006)

Sen, C.K., Khanna, S., Roy, S.: Tocotrienols in health and disease: the other half of the natural vitamin E family. Mol. Aspects Med. 28 (5–6), 692–728 (2007)

Traber, M.G., Atkinson, J.: Vitamin E, antioxidant and nothing more. Free Radical Biol. Med. 43 (1), 4–15 (2007)

US National Library of Medicine: Antioxidants.: http://www.nlm.nih.gov/medlineplus/antioxidants.html (2010). Accessed 26.05.2010

Neil, H.A.W., Meijer, G.W., Roe, L.S.: Randomised controlled trial of use by hypercholesterolaemic patients of a vegetable oil sterol-enriched fat spread. Atherosclerosis 156 (2), 329–337 (2001)

Holden, J.M., Eldridge, A.L., Beecher, G.R., Marilyn Buzzard, I., Bhagwat, S., Davis, C.S., Douglass, L.W., Gebhardt, S., Haytowitz, D., Schakel, S.: Carotenoid Content of U.S. foods: an update of the database. J. Food Compos. Anal. 12(3), 169–196 (1999)

Fernandes, P., Cabral, J.M.S.: Phytosterols: applications and recovery methods. Bioresour. Technol. 98 (12), 2335–2350 (2007)

Thurnham, D.D.I.: Functional foods: cholesterol-lowering benefits of plant sterols ? reply by Thurnham. Br. J. Nutr. 84 (02), 254 (2000). doi: 10.1017/S0007114500001501

Thurnham, D.I.: Functional foods: cholesterol-lowering benefits of plant sterols. Br. J. Nutr. 82 (04), 255–256 (1999). doi: 10.1017/S0007114599001440

Natura Vitalis.: Phytosterol—Kapseln u.a. mit Sägepalmextrakt. http://www.naturavitalis.de/index.php?page=shop.product_details&flypage=flypage.tpl&product_id=76&category_id=25&option=com_virtuemart&Itemid=2&gclid=CKOYs_W99KECFZaNzAodiCxoFA (2010). Accessed 31 May 2010

Fairvital.: Beta-Sitosterin 120 mg. http://www.fairvital.com/product_info.php?ref=6&products_id=56&gclid=CIPXpPS99KECFYWUzAodP1asEg (2009). Accessed 31 May 2010

Rote Liste Service: Rote Liste Online: Arzneimittelinformationen für Deutschland. http://www.rote-liste.de/Online/jumpsearch (2010). Accessed 31 May 2010

Starling, S.: Benecol smoothie courts younger cholesterol-lowering market. Food Drink Europe (2009)

Starling, S.: Sterol cookie launched in US. Functional Foods Nutraceuticals (2006)

Niemann, B., Sommerfeld, C., Hembeck, A., Bergmann, C.: Plant sterol enriched foods as perceived by consumers. In: BfR Wissenschaft. Federal Institute for Risk Assessment, Berlin, Germany, (2007)

McNeil Nutritionals.: Benecol—Proven to Reduce Cholesterol. http://www.benecolusa.com/index.jhtml (2010). Accessed 31 May 2010

Anonymous.: COMMISSION REGULATION (EC) No 983/2009 of 21 October 2009 on the authorisation and refusal of authorisation of certain health claims made on food and referring to the reduction of disease risk and to children’s development and health. In: The Commission of the European Communities (ed.), Commission Regulation (EC) No. 983/2009. pp. L 277/273–L 277/212 (2009)

Stones, M., Starling, S.: Plant stanol and sterol claims now law in EU. NutraIngredients (2009)

AbuMweis, S.S., Barake, R., Jones, P.J.H.: Plant sterols/stanols as cholesterol lowering agents: a meta-analysis of randomized controlled trials. Food Nutr. Res. (2008). doi: 10.3402/fnr.v52i0.1811

Starling, S.: Targeted and talkative: the Benecol success story. NutraIngredients (2009)

Ruiu, G., Pinach, S., Veglia, F., Gambino, R., Marena, S., Uberti, B., Alemanno, N., Burt, D., Pagano, G., Cassader, M.: Phytosterol-enriched yogurt increases LDL affinity and reduces CD36 expression in polygenic hypercholesterolemia. Lipids 44 (2), 153–160 (2009)

Ichiji, Y.: Cosmetics containing sulfated phytosterols. Japan Patent JP 2004250402, 20040909

Wachter, R., Tesmann, H., Behler, A., Maurer, K.-H.: Deodorizing Preparations. WO 98/17241

Meyer, H.: Cosmetic preparation for topical application, used for treating cellulite. Switzerland Patent WO 01/87257 A1

Eisfeld, W., Busch, P., Issberner, U., Biehl, P.: Haarpflegemittel mit natürlichen Ölen. Deutschland Patent DE 10126448 A1

Wachter, R., Salka, B., Magnet, A.: Phytosterole—pflanzliche Wirkstoffe in der Kosmetik. Parfümerie und Kosmetik 75 (11), 755–761 (1994)

European Food Safety Authority.: consumption of food and beverages with added plant sterols in the European Union. EFSA J. (2008) Nutraceuticals, pp. 1–21. Parma, Italy (2008)

U.S. Food and Drug Administration.: GRAS notice inventory: GRN No. 250—plant sterols and stanols from pine trees (2008)

Daniells, S.: Snack Size Science: Taking heart from phytosterol review. NutraIngredients (2009)

Daniells, S.: Phytosterols for cholesterol cuts supported by review. NutraIngredients (2009)

de Guzman, D.: Sterol investments on the rise. CMR ICIS publication 11–17 April 2005, 24 (2005)

Cognis: The future of healthy concepts. cognis connect—nutrition and health (2004)

Starling, S.: UK sterol foods market slows amid consumer confusion. NutraIngredients (2008)

Zeller, C.: Globalisierungsstrategien—Der Weg von Novartis. Springer, Berlin, Germany (2001)

Schaffnit-Chatterjee, C.: The global food equation: Food security in an environment of increasing scarcity. In: Schneider, S. (ed.) Current Issues, pp. 1–38. Deutsche Bank Research (2009)

Anonymous: Novartis, PepsiCo End Functional Foods Venture. http://www.allbusiness.com/retail-trade/food-stores/4245788-1.html (2002). Accessed 01 June 2010

Anonymous.: Novartis nimmt “Functional Food” vom Markt. Handelsblatt (2001)

Novartis: Novartis (1996–Gegenwart). http://www.novartis.ch/about-novartis/company-history/novartis-chronicle.shtml (2010). Accessed 01 June 2010

Runestad, T.: Cholesterol leads cardio category. Functional Ingredients (2009)

Ahlberg, P.: Phytosterol-based foods: Still seeking a foothold. http://www.just-food.com/analysis/still-seeking-a-foothold_id93676.aspx (2001). Accessed 30 June 2010

Starling, S.: Markets: functional chocolate finding its feet. Confectionery News (2009)

Mars: FAQs. https://cirkuhealth.com/FAQs.aspx (2010). Accessed 01 June 2010

Lea, L.J., Hepburn, P.A.: Safety evaluation of phytosterol-esters. Part 9: results of a European post-launch monitoring programme. Food Chem. Toxicol. 44 (8), 1213–1222 (2006)

Hilliam, M.: Functional food—how big is the market? World Food Ingredients 12 , 50–52 (2000)

Kotler, P., Armstrong, G., Wong, V., Saunders, J.: Principles of marketing, 5th European ed. Pearson Education, Harlow, UK (2008)

Siró, I., Kápolna, E., Kápolna, B., Lugasi, A.: Functional food. Product development, marketing and consumer acceptance—a review. Appetite 51 (3), 456–467 (2008)

Mark-Herbert, C.: Innovation of a new product category—functional foods. Technovation 24 (9), 713–719 (2004)

Bagchi, D. (ed.): Nutraceutical and functional food regulations in the United States and Around the World. Academic Press, San Diego (CA), USA (2008)

Boin, G., Conte-Salinas, N., Grube, M., Rusconi, G. (eds.): Functional Food: a synopsis of the legal framework in the European Union and selected other countries. Book on Demand, Norderstedt, Germany (2009)

Codex Alimentarius Commission.: Codex Alimentarius. http://www.codexalimentarius.net/web/index_en.jsp (2010). Accessed 03 June 2010

Ohama, H., Ikeda, H., Moriyama, H.: Health foods and foods with health claims in Japan. In: Bagchi, D. (ed.) Nutraceuticals and functional food regulations in the United States and Around the World, pp. 249–280. Academic Press, San Diego (CA), USA (2008)

European Commission.: General Food Law—Introduction. http://ec.europa.eu/food/food/foodlaw/index_en.htm (2010). Accessed 03 June 2010

European Commission: Food Supplements. http://ec.europa.eu/food/food/labellingnutrition/supplements/index_en.htm (2010). Accessed 03 June 2010

Crompton, S.: Hype or beautiful science? Times Online. http://women.timesonline.co.uk/tol/life_and_style/women/body_and_soul/article2645139.ece?print=yes&randnum=1208519464093 (2007). Accessed 18 Apr 2008

Newburger, A.E.: Cosmeceuticals: myths and misconceptions. Clin. Dermatol. 27 (5), 446–452 (2009)

Dureja, H., Kaushik, D., Gupta, M., Kumar, V., Lather, V.: Cosmeceuticals: an emerging concept. Indian J. Pharmacol. 37 (3), 155–159 (2005)

Krohn, M., Kleber, A., Schaffar, G., Dechert, U., Eck, J.: Now we are talking sense! Functional approaches to novel nutraceuticals and cosmeceuticals. Biotechnol. J. 3 (9–10), 1147–1156 (2008)

Bissett, D.L.: Common cosmeceuticals. Clin. Dermatol. 27 (5), 435–445 (2009)

Amer, M., Maged, M.: Cosmeceuticals versus pharmaceuticals. Clin. Dermatol. 27 (5), 428–430 (2009)

Council of the European Communities.: COUNCIL DIRECTIVE of 27 July 1976 on the approximation of the laws of the member states relating to cosmetic products, vol. 76/768/EEC (2008)

Beiersdorf: 2010 good-bye cellulite, hello bikini challenge. http://www.niveausa.com/highlights/local_highlight/local_gbc2010challenge (2010). Accessed 03 June 2010

Paul, L.C.: Cosmeceuticals and patents: the physician and dentist brands. Clin. Dermatol. 27 (5), 507–512 (2009)

U.S. Food and Drug Administration: FDA Authority Over Cosmetics. http://www.fda.gov/Cosmetics/GuidanceComplianceRegulatoryInformation/ucm074162.htm (2005). Accessed 03 June 2010

U.S. Food and Drug Administration: Cosmetic Labeling & Label Claims. http://www.fda.gov/Cosmetics/CosmeticLabelingLabelClaims/default.htm (2006). Accessed 03 June 2010

U.S. Food and Drug Administration: “Cosmeceutical”. http://www.fda.gov/Cosmetics/ProductandIngredientSafety/ProductInformation/ucm127064.htm (2000). Accessed 03 June 2010

U.S. Food and Drug Administration: Is It a Cosmetic, a Drug, or Both? (Or Is It Soap?). http://www.fda.gov/Cosmetics/GuidanceComplianceRegulatoryInformation/ucm074201.htm (2002). Accessed 03 June 2010

Draelos, Z.D.: Cosmeceuticals: undefined, unclassified, and unregulated. Clin. Dermatol. 27 (5), 431–434 (2009)

de Guzman, D.: Global economy to limit cosmetic sales in 2009—Euromonitor. http://www.icis.com/Articles/2009/04/03/9206067/global-economy-to-limit-cosmetic-sales-in-2009-euromonitor.html (2009). Accessed 03 June 2010

Kumar, S.: Exploratory analysis of global cosmetic industry: major players, technology and market trends. Technovation 25 (11), 1263–1272 (2005)

Matthews, I.: The Growing Trend for Cosmeceuticals. http://www.in-cosmetics.com/page.cfm/Link=445/t=m (2010). Accessed 03 June 2010

Morganti, P.: Reflections on cosmetics, cosmeceuticals, and nutraceuticals. Clin. Dermatol. 26 (4), 318–320 (2008)

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Curran, CS. (2013). Conceptual Framework and Research Design. In: The Anticipation of Converging Industries. Springer, London. https://doi.org/10.1007/978-1-4471-5170-8_4

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