descriptive research design conceptual framework

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Conceptual Framework for Descriptive Design

• October 8, 2021

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The term “conceptual framework” is used to refer to the structure of your research. It defines the research objective and maps out how the process comes together to draw meaningful conclusion. The framework demonstrates the relationship between the variables. 

In this blog, we will discuss more about what conceptual framework is, its importance in descriptive research, and the four steps to write it.

What is a Conceptual Framework?

In research, it refers to a visual or written representation of the expected relationship between the variables being studied. It depicts what a researcher expects to find through their research and clearly maps out the steps that must be carried out through the course of the study. 

As the conceptual framework illustrates a researcher’s understanding of how variables connect, it can be used to identify the key variables that need to be investigated. It acts as a “map” that provides researchers with a shape and structure to their research, helping them carry out their study more effectively.

Let’s look at what makes it important for a descriptive research.

Why use a Conceptual Framework for Descriptive Research?

This type of framework should be constructed before the data collection process is commenced so as to guide the investigation. These are a few advantages of using a it for descriptive research: 

• Provides a framework that can be used to make decisions and to solve new and emerging practical problems.
• Can be used by organizations to enhance financial reporting by making it easier to understand for users. 
• Can be used to enhance the compatibility of a company’s financial statements with competitors. 
• It focuses on the theory of a phenomenon and can provide a base and structure for research to be built on.
• It is a quick and cost-effective form of research as it does not require experimentation and instead relies on information obtained through previously conducted studies.

This concludes the importance of using conceptual framework in descriptive research. Let’s see how the data you want to collect plays a role in the research design.

Using Qualitative and Quantitative Data in the Conceptual Framework

The way you formulate your research design will vary depending on the nature of the data being analysed. Let’s look at how the the framework will vary when used with qualitative data in comparison to when it is used with quantitative data. 

Qualitative data is used when a research problem focuses on meanings, perceptions, and/or descriptions of the research topic. When analysed, qualitative data allows researchers to understand behaviours, interactions, situations, and contexts. Researchers can then analyse these observations to identify correlations, patterns, and categories. 

Quantitative data is used when a research problem requires numerical values associated with the variables being studied, whether they be traits, trends, or characteristics. When analysed, quantitative data can illustrate any outliers among the data. The numerical values can even depict the degree of difference or relationship between the conceptual variables being studied in the conceptual framework. 

Additionally, the data can help determine whether the findings are generalizable to the larger population or whether they are only true for the sample.

What are the conceptual variables?

To designing the framework of your research it is best to also learn about the conceptual variables. There are four types of variable as mentioned below. 

Independent and Dependent Variables

When testing the cause-and-effect relationship, there are two key variables we must identify: the independent variable and the dependent variable. 

• Independent Variable: The variable that the researcher manipulates, and is assumed to influence the dependent variable.
• Dependent Variable: The variables being tested and measured, and is thought to be ‘dependent’ on the value of the independent variable.

Moderating and Mediating Variables

As we expand our framework, there are other important variables to consider, specifically moderating and mediating variables. 

• Moderating Variables: Also known as moderators. They alter or influence the effect that an independent variable has on the dependent variable. Therefore, these variables change the effect component of the cause-and-effect relationship between the independent and dependent variables. Moderation can also be referred to as the interaction effect.
• Mediating Variables: Also known as a mediator. A mediating variable is a variable that links the independent and dependent variables, and its existence helps explain the relationship between them.

Now that we have established some groundwork around the topic let’s see how you can write the framework for the descriptive research.

4 Steps on How to Make a Conceptual Framework

The framework represents the expected relationship between the variables of your research. The framework is used to generate the research hypothesis to generate conclusion. 

We have outlined four steps on how you write a framework for your descriptive research.

• Outline your topic.
• Conduct a literature review. 
• Isolate key conceptual variables. 
• Generate the framework. 

Let’s explain these steps further.  

1. Outline your topic

The first step in creating your conceptual framework is to clearly outline your topic of research. In this step, you must decide on your topic of research. It is important to select a topic that relates to your field of specialization so that you will be familiar with the different aspects of your study. 

2. Conduct a literature review

A literature review involves the search and evaluation of the available and relevant literature in a chosen topic area. In this step, you must review and synthesise well-known scientific journals and research papers, preferably those that are peer-reviewed, to gain a deep and comprehensive understanding of the research problem at hand. 

3. Isolate the key conceptual variables

Once you’ve carried out the literature review, you should have an idea of the most important and relevant variables to your research topic. This brings us to the third step which requires you to isolate the key variables described in the literature and then determine how these variables relate to each other. 

In research papers, you will generally find the key variables outlined in the abstract, methodology, and summary section. The summary in research papers should also provide you with an understanding of how these variables work together.

4. Generate the conceptual framework

Your fourth and final step is to generate your framework. Now that you’ve identified your most important variables and have a general idea of how they relate to each other, you can begin creating your framework. Your problem statement should serve as a reference for constructing it.

Follow these four steps to create a framework that better represents that relationship between the variables. 

While this concludes all there is to know about conceptual framework for descriptive research but for better understanding we have also compared it agains thoretrical framework.

Theoretical Vs. Conceptual Framework

Both these framework define the structure of your research. However, there are differences between the two framework which we will discuss here. 

Theoretical framework: 

It introduces a theory that gives shape to the research problem. So, the theoretical framework is drawn from theory from existing works. 

Conceptual framework:

It can be considered as a roadmap for your research. The framework helps you visualize your research to put it into action. It is more about the approach you adopt to answer the research question.

Wrapping up;

This sums up all you need to know about conceptual framework for descriptive research. It is important that you develop the framework so that you can visual the research and link different concepts.

FAQs on Conceptual Framework

In descriptive research, the theoretical framework is a single formal theory that acts as a synthesis of the theory developed regarding a research problem. When a study is designed using the theoretical frameworks, the theory is the primary means by which the research problem is investigated.

A conceptual framework can be defined as an outline of the expected relationship between the variables being studied. It illustrates the researcher’s understanding of how the variables relate to each other.

When using the conceptual framework in quantitative research, it is vital to define the research problem, as well as the key variables that can be used to resolve it. This is in contrast to qualitative research, wherein researchers aim to build up a theory so the conceptual framework emerges after the research is complete.

When using the conceptual framework in qualitative research, an inductive approach is taken. This means that the researcher builds up the theory and only establishes a conceptual framework once the research is complete.

The main purpose of the conceptual framework is to define all the relevant variables being studied and to illustrate how they may be related to each other. It provides a visual format using which researchers can carry out their study more effectively.

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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• 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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Qualitative Descriptive Methods in Health Science Research

Karen jiggins colorafi.

1 College of Nursing & Health Innovation, Arizona State University, Phoenix, AZ, USA

Bronwynne Evans

The purpose of this methodology paper is to describe an approach to qualitative design known as qualitative descriptive that is well suited to junior health sciences researchers because it can be used with a variety of theoretical approaches, sampling techniques, and data collection strategies.

Background:

It is often difficult for junior qualitative researchers to pull together the tools and resources they need to embark on a high-quality qualitative research study and to manage the volumes of data they collect during qualitative studies. This paper seeks to pull together much needed resources and provide an overview of methods.

A step-by-step guide to planning a qualitative descriptive study and analyzing the data is provided, utilizing exemplars from the authors’ research.

This paper presents steps to conducting a qualitative descriptive study under the following headings: describing the qualitative descriptive approach, designing a qualitative descriptive study, steps to data analysis, and ensuring rigor of findings.

Conclusions:

The qualitative descriptive approach results in a summary in everyday, factual language that facilitates understanding of a selected phenomenon across disciplines of health science researchers.

There is an explosion in qualitative methodologies among health science researchers because social problems lend themselves toward thoughtful exploration, such as when issues of interest are complex, have variables or concepts that are not easily measured, or involve listening to populations who have traditionally been silenced ( Creswell, 2013 ). Creswell (2013 , p. 48) suggests qualitative research is preferred when health science researchers seek to (a) share individual stories, (b) write in a literary, flexible style, (c) understand the context or setting of issues, (d) explain mechanisms or linkages in causal theories, (e) develop theories, and (f) when traditional quantitative statistical analyses do not fit the problem at hand. Typically, qualitative textbooks present learners with five approaches for qualitative inquiry: narrative, phenomenological, grounded theory, case study, and ethnography. Yet eminent researcher Margarete Sandelowski argues that in “the now vast qualitative methods literature, there is no comprehensive description of qualitative description as a distinctive method of equal standing with other qualitative methods, although it is one of the most frequently employed methodological approaches in the practice disciplines” ( Sandelowski, 2000 ). Qualitative description is especially amenable to health environments research because it provides factual responses to questions about how people feel about a particular space, what reasons they have for using features of the space, who is using particular services or functions of a space, and the factors that facilitate or hinder use.

The purpose of this methodology article is to define and outline qualitative description for health science researchers, providing a starter guide containing important primary sources for those who wish to become better acquainted with this methodological approach.

Describing the Qualitative Descriptive Approach

In two seminal articles, Sandelowski promotes the mainstream use of qualitative description ( Sandelowski, 2000 , 2010 ) as a well-developed but unacknowledged method which provides a “comprehensive summary of an event in the every day terms of those events” ( Sandelowski, 2000 , p. 336). Such studies are characterized by lower levels of interpretation than are high-inference qualitative approaches such as phenomenology or grounded theory and require a less “conceptual or otherwise highly abstract rendering of data” ( Sandelowski, 2000 , p. 335). Researchers using qualitative description “stay closer to their data and to the surface of words and events” ( Sandelowski, 2000 , p. 336) than many other methodological approaches. Qualitative descriptive studies focus on low-inference description, which increases the likelihood of agreement among multiple researchers. The difference between high and low inference approaches is not one of rigor but refers to the amount of logical reasoning required to move from a data-based premise to a conclusion. Researchers who use qualitative description may choose to use the lens of an associated interpretive theory or conceptual framework to guide their studies, but they are prepared to alter that framework as necessary during the course of the study ( Sandelowski, 2010 ). These theories and frameworks serve as conceptual hooks upon which hang study procedures, analysis, and re-presentation. Findings are presented in straightforward language that clearly describes the phenomena of interest.

Other cardinal features of the qualitative descriptive approach include (a) a broad range of choices for theoretical or philosophical orientations, (b) the use of virtually any purposive sampling technique (e.g., maximum variation, homogenous, typical case, criterion), (c) the use of observations, document review, or minimally to moderately structured interview or focus group questions, (d) content analysis and descriptive statistical analysis as data analysis techniques, and (e) the provision of a descriptive summary of the informational contents of the data organized in a way that best fits the data ( Neergaard, Olesen, Andersen, & Sondergaard, 2009 ; Sandelowski, 2000 , 2001 , 2010 ).

Designing a Qualitative Descriptive Study

Methodology.

Unlike traditional qualitative methodologies such as grounded theory, which are built upon a particular, prescribed constellation of procedures and techniques, qualitative description is grounded in the general principles of naturalistic inquiry. Lincoln and Guba suggest that naturalistic inquiry deals with the concept of truth, whereby truth is “a systematic set of beliefs, together with their accompanying methods” ( Lincoln & Guba, 1985 , p. 16). Using an often eclectic compilation of sampling, data collection, and data analysis techniques, the researcher studies something in its natural state and does not attempt to manipulate or interfere with the ordinary unfolding of events. Taken together, these practices lead to “true understanding” or “ultimate truth.” Table 1 describes design elements in two exemplar qualitative descriptive studies and serves as guide to the following discussion.

Example of Study Design Elements for Two Studies.

Theoretical Framework

Theoretical frameworks serve as organizing structures for research design: sampling, data collection, analysis, and interpretation, including coding schemes, and formatting hypothesis for further testing ( Evans, Coon, & Ume, 2011 ; Miles, Huberman, & Saldana, 2014 ; Sandelowski, 2010 ). Such frameworks affect the way in which data are ultimately viewed; qualitative description supports and allows for the use of virtually any theory ( Sandelowski, 2010 ). Creswell’s chapter on “Philosophical Assumptions and Interpretative Frameworks” (2013) is a useful place to gain understanding about how to embed a theory into a study.

Sampling choices place a boundary around the conclusions you can draw from your qualitative study and influence the confidence you and others place in them ( Miles et al., 2014 ). A hallmark of the qualitative descriptive approach is the acceptability of virtually any sampling technique (e.g., maximum variation where you aim to collect as many different cases as possible or homogenous whereby participants are mostly the same). See Miles, Huberman, and Saldana’s (2014 , p. 30) “Bounding the Collection of Data” discussion to select an appropriate and congruent purposive sampling strategy for your qualitative study.

Data Collection

In qualitative descriptive studies, data collection attempts to discover “the who, what and where of events” or experiences ( Sandelowski, 2000 , p.339). This includes, but is not limited to focus groups, individual interviews, observation, and the examination of documents or artifacts.

Data Analysis

Content analysis refers to a technique commonly used in qualitative research to analyze words or phrases in text documents. Hsieh and Shannon (2005) present three types of content analysis, any of which could be used in a qualitative descriptive study. Conventional content analysis is used in studies that aim to describe a phenomenon where exiting research and theory are limited. Data are collected from open-ended questions, read word for word, and then coded. Notes are made and codes are categorized. Directed content analysis is used in studies where existing theory or research exists: it can be used to further describe phenomena that are incomplete or would benefit from further description. Initial codes are created from theory or research and applied to data and unlabeled portions of text are given new codes. Summative content analysis is used to quantify and interpret words in context, exploring their usage. Data sources are typically seminal texts or electronic word searches.

Quantitative data can be included in qualitative descriptive studies if they aim to more adequately or fully describe the participants or phenomenon of interest. Counting is conceptualized as a “means to and end, not the end itself” by Sandelowski (2000 , p. 338) who emphasizes that careful descriptive statistical analysis is an effort to understand the content of data, not simply the means and frequencies, and results in a highly nuanced description of the patterns or regularities of the phenomenon of interest ( Sandelowski, 2000 , 2010 ). The use of validated measures can assist with generating dependable and meaningful findings, especially when the instrument (e.g., survey, questionnaire, or list of questions) used in your study has been used in others, helping to build theory, improve predictions, or make recommendations ( Miles et al., 2014 ).

Data Re-Presentation

In clear and simple terms, the “expected outcome of qualitative descriptive studies is a straight forward descriptive summary of the informational contents of data organized in a way that best fits the data” ( Sandelowski, 2000 , p. 339). Data re-presentation techniques allow for tremendous creativity and variation among researchers and studies. Several good resources are provided to spur imagination ( Miles et al., 2014 ; Munhall & Chenail, 2008 ; Wolcott, 2009 ).

Steps to Data Analysis

It is often difficult for junior health science researchers to know what to do with the volumes of data collected during a qualitative study and formal course work in traditional qualitative methods courses are typically sparse regarding the specifics of data management. It is for those reasons that this section of our article will provide a detailed description of the data analysis techniques used in qualitative descriptive methodology. The following steps are case examples of a study undertaken by one author (K.J.C.) after completing a data management course offered by another author (B.E.). Examples are offered from the two studies noted in Table 1 . It is offered in list format for general readability, but the qualitative researcher should recognize that qualitative analyses are iterative and recursive by nature.

Example of a Coding Manual.

Note . SES = socioeconomic status.

Reading from the left in Table 2 , codes were given a number and letter for use in marking sections of text. Next, the code name indicating a theme was entered in boldface type with a definition in the code immediately under it. The second column provided an exemplar of each code, along with a notation indicating where it was found in the data, so that coders could recognize instances of that particular code when they saw them.

The coding manual was tested against data gathered in a preliminary study and was revised as codes found to overlap or be missing entirely. We continued to revise it iteratively during the study as data collection and analysis proceeded and then used it to recode previously coded data. Using this procedure, it was used to revisit the data several times.

• Each transcribed document was formatted with wide right margins that allowed the investigator to apply codes and generate marginal remarks by hand. Marginal remarks are handwritten comments entered by the investigator. They represent an attempt to stay “alert” about analysis, forming ideas and recording reactions to the meaning of what is seen in the data. Marginal remarks often suggest new interpretations, leads, and connections or distinctions with other parts of the data ( Miles et al., 2014 ). Such remarks are preanalytic and add meaning and clarity to transcripts.

Level 1 Coding With Meaning Units.

• Conceptually similar codes were organized into categories (coding groups of coded themes that were increasingly abstract) through revisiting the theory framing the study (asking, “does this system of coding make sense according to the chosen theory?”). Miles et al. (2014) provide many examples for creating, categorizing, and revising codes, including highlighting a technique used by Corbin and Strauss ( Corbin & Strauss, 2015 ) that includes growing a list of codes and then applying a slightly more abstract label to the code, creating new categories of codes with each revision. This is often referred to as second-level or pattern coding, a way of grouping data into a smaller number of sets, themes, or constructs. During the analysis of data, patterns were generated and the researcher spent significant amounts of time with different categorizations, asking questions, checking relationships, and generally resisting the urge to be “locked too quickly into naming a pattern” ( Miles et al., 2014 , p. 69).
• During this phase of analysis, pattern codes were revised and redefined in the coding manual and exemplars were used to clarify the understanding of each code. Miles et al. (2014) suggest that software can be helpful during this categorization (counting) step, so lists of observed engagement behaviors were also recorded in Dedoose software ( Dedoose, 2015 ) by code so that frequencies could be captured and analyzed. Despite the assistance of Dedoose, the researcher found that hand sorting codes into themes and categories was best done on paper.

Example of an analytic memo used in qualitative description analysis.

Data Matrix.

Note . The CLOX is an executive clock drawing task that tests cognition and was used in this study with the caregiver (CG) and the care recipient (CR). The CG Strain and the CG Gain scores were derived by the researcher through a qualitative content analysis ( Evans, Coon, & Belyea, 2006 ).

• Finally, the data are re-presented in a creative but rigorous way that are judged to best fit the findings ( Miles et al., 2014 ; Sandelowski & Leeman, 2012 ; Stake, 2010 ; Wolcott, 2009 ).

Strategies for Ensuring Rigor of Findings

Many qualitative researchers do not provide enough information in their reports about the analytic strategies used to ensure verisimilitude or the “ring of truth” for the conclusions. Miles, Huberman, and Saldana (2014) outline 13 tactics for generating meaning from data and another 13 for testing or confirming findings. They also provide five standards for assessing the quality of conclusions. The techniques relied upon most heavily during a qualitative descriptive study ought to be addressed within the research report. It is important to establish “trustworthiness” and “authenticity” in qualitative research that are similar to the terms validity and reliability in quantitative research. The five standards (objectivity, dependability, credibility, transferability, and application) typically used in qualitative descriptive studies to assess quality and legitimacy (trustworthiness and authenticity) of the conclusions are discussed in the next sections ( Lincoln & Guba, 1985 ; Miles et al., 2014 ).

Objectivity

First, objectivity (confirmability) is conceptualized as relative neutrality and reasonable freedom from researcher bias and can be addressed by (a) describing the study’s methods and procedures in explicit detail, (b) sharing the sequence of data collection, analysis, and presentation methods to create an audit trail, (c) being aware of and reporting personal assumptions and potential bias, (d) retaining study data and making it available to collaborators for evaluation.

Dependability

Second, dependability (reliability or auditability) can be fostered by consistency in procedures across participants over time through various methods, including the use of semistructured interview questions and an observation data collection worksheet. Quality control ( Miles et al., 2014 ) can be fostered by:

• deriving study procedures from clearly outlined research questions and conceptual theory, so that data analysis could be linked back to theoretical constructs;
• clearly describing the investigator’s role and status at the research site;
• demonstrating parallelism in findings across sources (i.e., interview vs. observation, etc.);
• triangulation through the use of observations, interviews, and standardized measures to more adequately describe various characteristics of the sample population ( Denzin & Lincoln, 1994 );
• demonstrating consistency in data collection for all participants (i.e., using the same investigator and preprinted worksheets, asking the same questions in the same order);
• developing interview questions and observation techniques based on theory, revised, and tested during preliminary work;
• developing a coding manual a priori to guide data analysis, containing a “start list” of codes derived from the theoretical framework and relevant literature ( Fonteyn et al., 2008 ; Hsieh & Shannon, 2005 ; Miles et al., 2014 ); and
• developing a monitoring plan (fidelity) to ensure that junior researchers, especially do not go “beyond the data” ( Sandelowski, 2000 ) in interpretation. In keeping with the qualitative tradition, data analysis and collection should occur simultaneously, giving the investigator the opportunity to correct errors or make revisions.

Credibility

Third, credibility or verisimilitude (internal validity) is defined as the truth value of data: Do the findings of the study make sense ( Miles et al., 2014 , p. 312). Credibility in qualitative work promotes descriptive and evaluative understanding, which can be addressed by (a) providing context-rich “thick descriptions,” that is, the work of interpretation based on data ( Sandelowski, 2004 ), (b) checking with other practitioners or researchers that the findings “ring true,” (c) providing a comprehensive account, (d) using triangulation strategies, (e) searching for negative evidence, and (f) linking findings to a theoretical framework.

Transferability

Fourth, transferability (external validity or “fittingness”) speaks to whether the findings of your study have larger import and application to other settings or studies. This includes a discussion of generalizability. Sample to population generalizability is important to quantitative researchers and less helpful to qualitative researchers who seek more of an analytic or case-to-case transfer ( Miles et al., 2014 ). Nonetheless, transferability can be aided by (a) describing the characteristics of the participants fully so that comparisons with other groups may be made, (b) adequately describing potential threats to generalizability through sample and setting sections, (c) using theoretical sampling, (d) presenting findings that are congruent with theory, and (e) suggesting ways that findings from your study could be tested further by other researchers.

Application

Finally, Miles et al. (2014) speak to the utilization, application, or action orientation of the data. “Even if we know that a study’s findings are valid and transferable,” they write, “we still need to know what the study does for its participants and its consumers” ( Miles et al., 2014 , p. 314). To address application, findings of qualitative descriptive studies are typically made accessible to potential consumers of information through the publication of manuscripts, poster presentations, and summary reports written for consumers. In addition, qualitative descriptive study findings may stimulate further research, promote policy discussions, or suggest actual changes to a product or environment.

Implications for Practice

The qualitative description clarified and advocated by Sandelowski (2000 , 2010 ) is an excellent methodological choice for the healthcare environments designer, practitioner, or health sciences researcher because it provides rich descriptive content from the subjects’ perspective. Qualitative description allows the investigator to select from any number of theoretical frameworks, sampling strategies, and data collection techniques. The various content analysis strategies described in this paper serve to introduce the investigator to methods for data analysis that promote staying “close” to the data, thereby avoiding high-inference techniques likely challenging to the novice investigator. Finally, the devotion to thick description (interpretation based on data) and flexibility in the re-presentation of study findings is likely to produce meaningful information to designers and healthcare leaders. The practical, step-by-step nature of this article should serve as a starting guide to researchers interested in this technique as a way to answer their own burning questions.

Acknowledgments

The author would like to recognize the other members of her dissertation committee for their contributions to the study: Gerri Lamb, Karen Dorman Marek, and Robert Greenes.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research assistance for data analysis and manuscript development was supported by training funds from the National Institutes of Health/National Institute on Nursing Research (NIH/NINR), award T32 1T32NR012718-01 Transdisciplinary Training in Health Disparities Science (C. Keller, P.I.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NINR. This research was supported through the Hartford Center of Gerontological Nursing Excellence at Arizona State University College of Nursing & Health Innovation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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The Anticipation of Converging Industries pp 127–171 Cite as

Conceptual Framework and Research Design

• Clive-Steven Curran 2  
• First Online: 01 January 2013

935 Accesses

1 Citations

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 ].

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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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