research framework and hypothesis

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Theoretical Framework
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

• It is logical and coherent
• It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
• It has a domain where it applies
• It has clearly described relationships among variables
• It describes, explains, and makes specific predictions
• It comprises of concepts, themes, principles, and constructs
• It must have been based on empirical data [i.e., it is not a guess]
• It must have made claims that are subject to testing, been tested and verified
• It must be clear and concise
• Its assertions or predictions must be different and better than those in existing theories
• Its predictions must be general enough to be applicable to and understood within multiple contexts
• Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
• The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
• Its concepts and principles explain what is going on and why
• Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
• The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
• Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
• The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

• Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
• The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
• Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

• << Previous: The Research Problem/Question
• Next: 5. The Literature Review >>
• Last Updated: Sep 4, 2024 9:40 AM
• URL: https://libguides.usc.edu/writingguide

• Subscribe to journal Subscribe
• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Problem Statement, Conceptual Framework, and Research Question

McGaghie, William C.; Bordage, Georges; Shea, Judy A. *

* Lloyd Lewis, PhD, emeritus professor of the Medical College of Georgia, participated in early meetings of the Task Force and contributed to the earliest draft of this section.

REVIEW CRITERIA

• The introduction builds a logical case and context for the problem statement.
• The problem statement is clear and well articulated.
• The conceptual (theoretical) framework is explicit and justified.
• The research question (research hypothesis where applicable) is clear, concise, and complete.
• The variables being investigated are clearly identified and presented.

ISSUES AND EXAMPLES RELATED TO THE CRITERIA

Introduction.

A scholarly manuscript starts with an Introduction that tells a story. The Introduction orients the reader to the topic of the report, moving from broad concepts to more specific ideas. 1 The Introduction should convince the reader, and all the more the reviewer, that the author has thought the topic through and has developed a tight, “researchable” problem. The Introduction should move logically from the known to the unknown. The actual components of an Introduction (including its length, complexity, and organization) will vary with the type of study being reported, the traditions of the research community or discipline in which it is based, and the style and tradition of the journal receiving the manuscript. It is helpful for the reviewer to evaluate the Introduction by thinking about its overall purpose and its individual components: problem statement, conceptual framework, and research question. Two related articles, “Reference to the Literature” and “Relevance,” follow the present article.

Problem Statement

The Introduction to a research manuscript articulates a problem statement . This essential element conveys the issues and context that gave rise to the study. Two examples of problem statements are: “With the national trend toward more patient care in outpatient settings, the numbers of patients on inpatient wards have declined in many hospitals, contributing to the inadequacy of inpatient wards as the primary setting for teaching students,” 2 and “The process of professional socialization, regardless of the philosophical approach of the educational program, can be stressful … few studies have explored the unique stressors associated with PBL in professional education.” 3 These statements help readers anticipate the goals of each study. In the case of the second example, the Introduction ended with the following statement: “The purpose of this qualitative study was to identify stressors perceived by physiotherapy students during their initial unit of study in a problem-based program.” In laying out the issues and context, the Introduction should not contain broad generalizations or sweeping claims that will not be backed up in the paper's literature review. (See the next article.)

Conceptual Framework

Most research reports cast the problem statement within the context of a conceptual or theoretical framework. 4 A description of this framework contributes to a research report in at least two ways because it (1) identifies research variables, and (2) clarifies relationships among the variables. Linked to the problem statement, the conceptual framework “sets the stage” for presentation of the specific research question that drives the investigation being reported. For example, the conceptual framework and research question would be different for a formative evaluation study than for a summative study, even though their variables might be similar.

Scholars argue that a conceptual or theoretical framework always underlies a research study, even if the framework is not articulated. 5 This may seem incongruous, because many research problems originate from practical educational or clinical activities. Questions often arise such as “I wonder why such an event did not [or did] happen?” For example, why didn't the residents' test-interpretation skills improve after they were given feedback? There are also occasions when a study is undertaken simply to report or describe an event, e.g., pass rates for women versus men on high-stakes examinations such as the United States Medical Licensing Examination (USMLE) Step 1. Nevertheless, it is usually possible to construct at least a brief theoretical rationale for the study. The rationale in the USMLE example may be, for instance, about gender equity and bias and why these are important issues. Frameworks are usually more elaborate and detailed when the topics that are being studied have long scholarly histories (e.g., cognition, psychometrics) where active researchers traditionally embed their empirical work in well-established theories.

Research Question

A more precise and detailed expression of the problem statement cast as a specific research question is usually stated at the end of the Introduction. To illustrate, a recent research report states, “The research addressed three questions. First, do students” pulmonary physiology concept structures change from random patterns before instruction to coherent, interpretable structures after a focused block of instruction? Second, can an MDS [multidimensional scaling] solution account for a meaningful proportion of variance in medical and veterinary students' concept structures? Third, do individual differences in the ways in which medical and veterinary students intellectually organize the pulmonary physiology concepts as captured by MDS correlate with course examination achievement? 6

In experimental research, the logic revealed in the Introduction might result in explicitly stated hypotheses that would include specification of dependent and independent variables. 7 By contrast, much of the research in medical education is not experimental. In such cases it is more typical to state general research questions. For example, “In this [book] section, the meaning of medical competence in the worlds of practicing clinicians is considered through the lens of an ethnographic story. The story is about the evolution of relationships among obstetrical providers and transformations in obstetrical practice in one rural town in California, which I will call ‘Coast Community,’ over the course of a decade.” 8

For some journals, the main study variables (e.g., medical competence) will be defined in the Introduction. Other journals will place this in the Methods section. Whether specific hypotheses or more general research questions are stated, the reviewer (reader) should be able to anticipate what will be revealed in the Methods.

The purpose of the Introduction is to construct a logical “story” that will educate the reader about the study that follows. The order of the components may vary, with the problem statement sometimes coming after the conceptual framework, while in other reports the problem statement may appear in the first paragraph to orient the reader about what to expect. However, in all cases the Introduction will engage, educate, and encourage the reader to finish the manuscript.

Section Description

Review Criteria for Research Manuscripts

Joint Task Force of Academic Medicine and the GEA-RIME Committee

• + Favorites
• View in Gallery

Readers Of this Article Also Read

The distinctions between theory, theoretical framework, and conceptual framework, toward a common taxonomy of competency domains for the health professions and..., finding, recruiting, and sustaining the future primary care physician..., a model of community-based interdisciplinary team training in the care of the....

What is a good example of a conceptual framework?

Last updated

18 April 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

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

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• The importance of a conceptual framework

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

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

Clarify research goals and objectives

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

Provide a theoretical basis for the study

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

Guide the research design

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

Here are some examples:

Confounding variables they hadn’t previously considered

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

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

• Steps to develop a conceptual framework

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

Step 1: Choose the research question

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

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

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

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

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

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

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

Step 2: Select the independent and dependent variables

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

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

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

Natural sciences

Independent variable: exposure to ultraviolet radiation

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

Health sciences

Independent variable: cognitive-behavioral therapy

Dependent variable: depression in adolescents

Independent variables: factors contributing to the business’s success

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

Independent variable: implementation of technology in the classroom

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

Step 3: Visualize the cause-and-effect relationship

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

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

The diagrams for our examples might be used as follows:

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

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

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

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

Step 4: Identify other influencing variables

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

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

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

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

Control variable: nutrient levels in the water

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

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

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

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

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

Control variable: industry competition

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

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

Control variable: student learning style

• Conceptual versus theoretical frameworks

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

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

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

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 22 August 2024

Last updated: 5 February 2023

Last updated: 16 August 2024

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

Quantitative Research in Mass Communications : R and RStudio

7 formulating research questions and hypotheses, 7.1 introduction to research questions and hypotheses.

In the realm of academic research, particularly within the field of mass communications, the formulation of research questions and hypotheses is a foundational step that sets the direction and scope of a study. These elements are crucial not only for guiding the research process but also for defining the study’s objectives and expectations. This section highlights the significance of research questions and hypotheses and elucidates the role they play in framing a study.

The Importance of Research Questions and Hypotheses in Guiding Research

Defining the Research Focus: Research questions serve as the cornerstone of any study, clearly outlining the specific issue or phenomenon that the research aims to explore. They help narrow down the broad area of interest into a focused inquiry that can be systematically investigated.

Guiding Methodology: The nature of the research question—whether it seeks to describe, compare, or determine cause and effect—directly influences the choice of research design, methods, and analysis techniques. Well-formulated questions ensure that the research methodology is appropriately aligned with the study’s objectives.

Facilitating Hypothesis Formulation: In quantitative research, hypotheses often stem from the research questions, proposing specific predictions or expectations based on theoretical foundations or previous studies. Hypotheses provide a testable statement that guides the empirical investigation and analysis.

7.1.1 Overview of the Role These Elements Play in Framing a Study

Structuring the Research Framework: Together, research questions and hypotheses establish the conceptual framework for a study, defining its boundaries and specifying the variables of interest. This framework serves as a blueprint, guiding all subsequent steps of the research process.

Informing Literature Review: Research questions and hypotheses inform the scope and focus of the literature review, directing attention to relevant theories, concepts, and empirical findings. This ensures that the review is tightly integrated with the study’s aims and contributes to building a solid theoretical foundation.

Determining Data Collection and Analysis: The formulation of research questions and hypotheses has direct implications for data collection methods, sampling strategies, and analytical techniques. They dictate what data are needed, how they should be collected, and the statistical tests or analytical approaches required to address the research questions and test the hypotheses.

Communicating the Study’s Purpose: Research questions and hypotheses effectively communicate the purpose and direction of the study to the academic community, stakeholders, and the broader public. They articulate the study’s contribution to knowledge, its relevance to theoretical debates or practical issues, and the potential implications of the findings.

In summary, research questions and hypotheses are indispensable components of the research process, serving as the guiding light for the entire study. They provide clarity, direction, and purpose, ensuring that the research is coherent, focused, and methodologically sound. By meticulously crafting these elements, researchers in mass communications lay the groundwork for meaningful and impactful studies that advance our understanding of complex media landscapes and communication dynamics.

7.2 Understanding Research Questions

Research questions are the foundation of any scholarly inquiry, guiding the direction and focus of the study. In mass communications research, where topics can range from analyzing media effects to understanding audience behaviors, formulating effective research questions is crucial for defining the scope and objectives of a study. This section delves into the definition and characteristics of a good research question, distinguishes between exploratory and descriptive research questions, and discusses strategies for developing clear and focused questions.

Definition and Characteristics of a Good Research Question

Definition: A research question is a clearly formulated question that outlines the issue or problem your study aims to address. It sets the stage for the research design, data collection, and analysis, directing the inquiry toward a specific goal.

Characteristics of a Good Research Question:

• Clarity: It should be clearly stated, avoiding ambiguity and ensuring that the research focus is understandable to others.
• Relevance: The question should be significant to the field of study, addressing gaps in the literature or emerging issues in mass communications.
• Researchability: It must be possible to answer the question through empirical investigation, using available research methods and tools.
• Specificity: A good question is specific, targeting a particular aspect of the broader topic to make the research manageable and focused.

Distinction Between Exploratory and Descriptive Research Questions

Exploratory Research Questions: These questions are used when little is known about the topic or phenomenon. Exploratory questions aim to investigate and gain insights into a subject, seeking to understand how or why something happens. In mass communications, an exploratory question might ask, “How do emerging social media platforms influence political engagement among young adults?”

Descriptive Research Questions: Descriptive questions aim to describe the characteristics or features of a subject. They are used when the goal is to provide an accurate representation or count of a phenomenon. A descriptive research question in mass communications might be, “What are the predominant themes in news coverage of environmental issues?”

Developing Clear and Focused Research Questions

• Specificity: Your research question should be narrowly tailored to address a specific issue within the broader field of mass communications. This specificity helps in defining the study’s scope and focusing the research efforts.
• Feasibility: Consider the practical aspects of answering your research question, including the availability of data, time constraints, and resource limitations. A feasible question is one that can be realistically investigated within the parameters of your study.
• Literature Review: Conduct a thorough review of existing research to identify gaps or unresolved questions in the field. This can inspire focused and relevant research questions.
• Consultation: Discuss your ideas with peers, mentors, or experts in mass communications. Feedback can help refine your questions and ensure they are both specific and feasible.
• Pilot Studies: Small-scale pilot studies or preliminary investigations can provide insights that help in formulating or refining your research questions.

Crafting clear and focused research questions is a critical step in the research process, setting the stage for meaningful and impactful inquiry. By ensuring that your questions are specific, feasible, and relevant to the field of mass communications, you lay the groundwork for a study that can contribute valuable insights to our understanding of media and communication phenomena.

7.3 Types of Research Questions

In the pursuit of scientific inquiry within mass communications, research questions serve as the navigational compass guiding the research process. These questions can be broadly categorized into two types: nondirectional and directional. Each type serves a distinct purpose and is formulated based on the nature of the study and the specific objectives the researcher aims to achieve. This section explores the definitions, uses, and strategies for crafting both nondirectional and directional research questions.

Nondirectional Research Questions

Definition: Nondirectional research questions are open-ended queries that explore the existence of a relationship between variables without specifying the anticipated direction of this relationship. They are used when the literature does not strongly suggest which outcome is expected or when exploring new or under-researched areas.

When to Use Them: Employ nondirectional questions when previous research is inconclusive, conflicting, or absent. They are particularly useful in exploratory studies where the aim is to uncover patterns, relationships, or phenomena without presupposing outcomes.

Crafting Questions:

• Focus on Exploration: Phrase your question to emphasize exploration, such as “Is there a relationship between social media usage and political participation among young adults?”
• Avoid Implied Direction: Ensure the wording does not inadvertently suggest a presumed direction of the relationship. The question should remain open to any outcome, whether positive, negative, or neutral.

Directional Research Questions

Definition: Directional research questions specify the expected direction of the relationship between variables. These questions are based on predictions that are often derived from theoretical frameworks or existing literature.

Purposes: Directional questions are used when there is sufficient theoretical or empirical basis to hypothesize a particular outcome. They guide the research towards testing specific hypotheses, making them suitable for studies aiming to confirm or refute theoretical predictions.

Formulating Questions:

• Specify Expected Outcomes: Clearly articulate the anticipated direction of the relationship in the question. For example, “Does increased exposure to environmental news lead to higher levels of environmental activism among viewers?”
• Ground in Literature: Ensure that the directionality implied by your question is supported by theoretical rationales or empirical evidence from previous research. This alignment strengthens the justification for expecting a particular outcome.

7.4 Strategies for Formulating Research Questions

Regardless of the type, crafting effective research questions requires a deep understanding of the topic at hand, a thorough review of the existing literature, and a clear articulation of the research’s goals. Here are some strategies to consider:

• Engage with Current Research: Immerse yourself in the latest studies and debates within the field of mass communications to identify trends, gaps, and areas ripe for investigation.
• Consult Theoretical Frameworks: Draw on established theories to guide the formulation of your questions, whether seeking to explore uncharted territory (nondirectional) or test specific propositions (directional).
• Iterative Refinement: Research questions often evolve during the initial stages of a study. Be prepared to refine your questions as you delve deeper into the literature and sharpen your study’s focus.

By thoughtfully selecting the type of research question that best suits the aims and scope of your study, you lay a solid foundation for a coherent, rigorous, and insightful exploration of mass communications phenomena.

7.5 Operationalization of Concepts

Operationalization is a critical process in the research design phase, particularly in quantitative studies within the realm of mass communications. It involves defining the abstract concepts or variables in measurable terms, determining how they will be observed, measured, or manipulated within the study. This section outlines the essence of operationalization, its pivotal role in research, the steps involved in operationalizing variables, and provides examples pertinent to mass communications research.

Defining Operationalization and Its Significance in Research

Definition: Operationalization is the process by which researchers define how to measure or manipulate the variables of interest in a study. It transforms theoretical constructs into measurable indicators, allowing for empirical observation and quantitative analysis.

Significance: The operationalization of concepts is fundamental to ensuring the reliability and validity of a study. By clearly specifying how variables are measured, researchers enable the replication of the study, enhance the clarity and coherence of their research design, and facilitate the objective analysis of findings.

Steps to Operationalize Variables

Identify the Key Concepts: Begin by clearly identifying the key concepts or variables you intend to study. In mass communications, this might include phenomena like media influence, audience engagement, or digital literacy.

Define the Variables Conceptually: Provide clear, conceptual definitions for each variable, drawing on existing literature or theoretical frameworks to delineate the boundaries of the concept.

Specify the Variables Operationally: Decide on the specific operations, techniques, or instruments you will use to measure or manipulate each variable. This includes determining the type of data to be collected, the scale of measurement, and the method of data collection.

Develop or Select Measurement Instruments: Choose or develop instruments that accurately measure your operationalized variables. This could involve creating surveys, designing experiments, or developing coding schemes for content analysis.

Pilot Test: Conduct a pilot test of your measurement instruments to ensure they effectively capture the operationalized variables. Adjustments based on feedback from the pilot test can improve the reliability and validity of the measures.

Examples of Operationalizing Common Variables in Mass Communications Research

Audience Engagement: Conceptually defined as the level of interaction and involvement an individual has with media content. Operationally, it could be measured through the number of social media shares, comments, or time spent viewing content.

Media Influence on Public Opinion: Conceptually, this refers to the impact media content has on shaping individuals’ attitudes and beliefs. Operationally, it could be measured by changes in attitudes before and after exposure to specific media messages, using pretest-posttest surveys.

Digital Literacy: Conceptually defined as the ability to find, evaluate, create, and communicate information using digital technologies. Operationally, digital literacy could be measured through a questionnaire assessing skills in these areas, with items rated on a Likert scale.

Operationalization is a cornerstone of rigorous research methodology, bridging the gap between theoretical concepts and empirical evidence. By meticulously defining and measuring variables, researchers in mass communications can ground their studies in observable reality, enhancing the validity of their findings and contributing meaningful insights into the complex dynamics of media and communication.

7.6 Developing Hypotheses

In the framework of quantitative research, particularly within the expansive field of mass communications, hypotheses serve as pivotal elements that further refine and operationalize the research questions. This section elucidates the definition and function of hypotheses in quantitative research, explores the relationship between research questions and hypotheses, and outlines the criteria that make a hypothesis testable.

Definition and Function of Hypotheses in Quantitative Research

Definition: A hypothesis is a predictive statement that proposes a possible outcome or relationship between two or more variables. It is grounded in theory or prior empirical findings and serves as a basis for scientific inquiry.

Function: The primary function of a hypothesis is to provide a specific, testable proposition derived from the broader research question. Hypotheses guide the research design, data collection, and analysis process, offering a clear focus for empirical investigation. They enable researchers to apply statistical methods to test the proposed relationships or effects, thereby contributing to the accumulation of scientific knowledge.

The Relationship Between Research Questions and Hypotheses

From Questions to Hypotheses: Research questions set the stage for the research by identifying the key phenomena or relationships of interest. Hypotheses take this a step further by specifying the expected direction or nature of these relationships based on theoretical or empirical groundwork. Essentially, while research questions identify “what” the study aims to explore, hypotheses propose “how” these explorations will unfold.

Complementarity: Research questions and hypotheses are complementary, with the former providing a broad inquiry framework and the latter offering a focused, conjectural answer that can be empirically tested. This synergy ensures that the research is both guided by curiosity and anchored in a framework that facilitates systematic investigation.

Criteria for a Testable Hypothesis

For a hypothesis to effectively contribute to the research process, it must be testable. The following criteria are essential for constructing a hypothesis that can be empirically evaluated:

Specificity: A testable hypothesis must clearly and specifically define the variables involved and the expected relationship between them. This clarity ensures that the hypothesis can be directly linked to observable and measurable outcomes.

Empirical Referents: The variables within the hypothesis must have empirical referents – that is, they must be capable of being measured or manipulated in the real world. This allows the hypothesis to be subjected to empirical testing.

Predictive Nature: A testable hypothesis should make a predictive statement about the expected outcome of the study, enabling the research to confirm or refute the proposed relationship or effect based on empirical evidence.

Grounding in Theory or Prior Research: The hypothesis should be grounded in existing theoretical frameworks or empirical findings, providing a rationale for the expected relationship or outcome. This grounding not only lends credibility to the hypothesis but also ensures that it contributes to the ongoing academic discourse.

Falsifiability: Finally, a testable hypothesis must be falsifiable. This means it should be possible to conceive of an outcome that would contradict the hypothesis, allowing for the possibility of it being disproven through empirical evidence.

Developing well-crafted hypotheses is a critical step in the quantitative research process, particularly in mass communications, where the rapid evolution of media technologies and platforms continually opens new avenues for inquiry. By adhering to these criteria, researchers can ensure that their hypotheses are not only testable but also meaningful, contributing valuable insights to our understanding of complex media landscapes and their impacts on society.

7.7 Types of Hypotheses

In the empirical research landscape, especially within the domain of mass communications, hypotheses are indispensable tools that guide the investigative process. They are typically categorized into null hypotheses and alternative hypotheses, each serving a distinct role in framing the research inquiry. This section provides definitions for these two types of hypotheses, discusses their roles in research, and offers guidance on formulating them effectively.

Null Hypotheses (H0)

Definition: The null hypothesis (H0) posits that there is no difference, effect, or relationship between the variables under investigation. It represents a statement of skepticism or neutrality, suggesting that any observed differences or relationships in the data are due to chance rather than a systematic effect.

Role in Research: The null hypothesis serves as a benchmark for testing the existence of an effect or relationship. By attempting to disprove or reject the null hypothesis through statistical analysis, researchers can provide evidence supporting the presence of a meaningful effect or relationship. The null hypothesis is foundational in hypothesis testing, enabling researchers to apply statistical methods to determine the likelihood that observed data could have occurred under the null condition.

Formulating Null Hypotheses: Null hypotheses are formulated as statements of no difference or no relationship. For example, in a study examining the impact of social media usage on political engagement, a null hypothesis might state, “There is no difference in political engagement levels between users and non-users of social media.”

Alternative Hypotheses (H1)

Definition: The alternative hypothesis (H1) is the counter proposition to the null hypothesis. It posits that there is a significant difference, effect, or relationship between the variables being studied. The alternative hypothesis reflects the researcher’s theoretical expectation or prediction about the outcome of the study.

Complementing Null Hypotheses: The alternative hypothesis directly complements the null hypothesis by specifying the expected effect or relationship that the research aims to demonstrate. While the null hypothesis posits the absence of an effect, the alternative hypothesis asserts its presence, guiding the direction of the study’s empirical investigation.

Crafting Alternative Hypotheses: Alternative hypotheses are crafted to predict specific outcomes based on the research question and theoretical framework. They should clearly articulate the anticipated direction or nature of the relationship or difference between variables. Continuing the earlier example, an alternative hypothesis might state, “Users of social media exhibit higher levels of political engagement than non-users.”

7.8 Strategic Formulation of Hypotheses

The formulation of null and alternative hypotheses is a strategic exercise that sets the stage for empirical testing. Effective hypotheses are:

• Specific and Concise: Clearly define the variables and the expected relationship or difference, avoiding ambiguity.
• Empirically Testable: Ensure that the hypotheses can be tested using available research methods and data.
• Theoretically Grounded: Base your hypotheses on existing literature, theories, or preliminary evidence, providing a rationale for the expected outcomes.

In mass communications research, where the interplay of media, technology, and society offers a rich tapestry of phenomena to explore, the thoughtful formulation of null and alternative hypotheses is crucial. It not only delineates the scope of the investigation but also ensures that the research contributes meaningful insights into the dynamics of communication processes and their impacts.

7.9 Directional and Nondirectional Hypotheses

In the nuanced world of quantitative research, particularly within the field of mass communications, hypotheses serve as a bridge between theoretical inquiry and empirical investigation. They are typically formulated as either directional or nondirectional, each with specific implications for the study’s design and analysis. This section clarifies the distinction between these two types of hypotheses and provides guidance on when to use each, complemented by examples from mass communications research.

Understanding the Distinction and When to Use Each Type

Directional Hypotheses: Directional hypotheses specify the expected direction of the relationship or difference between variables. They are based on theoretical predictions or empirical evidence suggesting a particular outcome. Directional hypotheses are used when prior research or theory provides a strong basis for anticipating the direction of the effect.

Nondirectional Hypotheses: Nondirectional hypotheses indicate that a relationship or difference exists between variables but do not specify the direction. They are appropriate when there is uncertainty about the expected outcome or when previous studies have yielded mixed or inconclusive results.

Examples of Both Directional and Nondirectional Hypotheses in Mass Communications Research

• “Individuals who frequently engage with news content on social media platforms will exhibit higher levels of political awareness than those who do not engage with news content on these platforms.” This hypothesis predicts a specific direction of the relationship between social media news engagement and political awareness.
• “Exposure to environmental documentaries will increase viewers’ concern for environmental issues more than exposure to traditional news coverage of the same issues.” This hypothesis specifies an expected difference in the effect of two types of media content on environmental concern.
• “There is a relationship between the frequency of smartphone use for social media and the level of social isolation experienced by young adults.” This hypothesis suggests a relationship exists but does not predict whether more frequent use increases or decreases social isolation.
• “The introduction of interactive digital learning tools in communication courses affects students’ academic performance.” This hypothesis indicates that an effect is expected but does not specify whether the effect is positive or negative on academic performance.

7.10 Deciding Between Directional and Nondirectional Hypotheses

The choice between directional and nondirectional hypotheses hinges on several factors:

• Theoretical Basis: Strong theoretical foundations or extensive empirical evidence supporting a specific outcome favor the use of directional hypotheses.
• Research Objectives: Exploratory studies aiming to identify patterns or relationships might initially employ nondirectional hypotheses, especially in emerging areas of mass communications where less is known.
• Statistical Considerations: Directional hypotheses allow for more focused statistical tests (e.g., one-tailed tests), which can be more powerful in detecting specified effects. However, they require a strong justification for predicting the direction of the effect.

By carefully considering these factors, researchers in mass communications can effectively choose the type of hypothesis that best suits their study’s objectives and theoretical framework. Whether directional or nondirectional, the formulation of hypotheses is a critical step in the research process, guiding empirical inquiry and contributing to the advancement of knowledge in the dynamic field of mass communications.

7.11 Criteria for Good Research Questions and Hypotheses

In the rigorous academic landscape of mass communications research, the construction of research questions and hypotheses serves as the bedrock upon which studies are built and conducted. These foundational elements not only guide the direction of the research but also determine its scope, focus, and potential contribution to the field. To ensure the effectiveness and integrity of research, certain criteria must be met. This section outlines the essential qualities of good research questions and hypotheses: clarity and precision, relevance to the field of study, and researchability with empirical testing potential.

Clarity and Precision

Definition: Clarity in research questions and hypotheses means that they are stated in a straightforward and unambiguous manner, easily understood by those within and outside the field. Precision involves the specific delineation of the variables and constructs involved, leaving no room for misinterpretation.

Importance: Clear and precise formulations allow for a focused investigation, guiding the research design, data collection, and analysis process. They ensure that the study addresses the intended concepts and relationships directly and effectively.

Strategies for Achieving Clarity and Precision:

• Use specific, defined terms and avoid jargon that may not be universally understood.
• Clearly specify the variables or phenomena being studied and their expected relationships.
• Ensure that hypotheses are directly testable, with defined criteria for confirmation or refutation.

Relevance to the Field of Study

Definition: Relevance implies that the research questions and hypotheses address significant issues, gaps, or debates within the field of mass communications. They should contribute to advancing understanding, theory, or practice in meaningful ways.

Importance: Research that is relevant to the field is more likely to receive attention from scholars, policymakers, and practitioners, and to secure funding and publication opportunities. It ensures that the study contributes to the ongoing discourse and development of mass communications as a discipline.

Strategies for Ensuring Relevance:

• Conduct a thorough review of current literature to identify gaps, emerging trends, or unresolved questions.
• Align research questions and hypotheses with theoretical frameworks or pressing societal issues.
• Consider the practical implications and potential impact of the research on the field.

Researchability and Empirical Testing Potential

Definition: Researchability refers to the feasibility of addressing the research questions and testing the hypotheses through empirical methods. This includes the availability of data, appropriateness of methodology, and the potential for gathering evidence to support or refute the hypotheses.

Importance: For research to contribute to the body of knowledge, it must be capable of being rigorously investigated using empirical methods. Research questions and hypotheses with high empirical testing potential allow for the derivation of meaningful, verifiable insights.

Strategies for Enhancing Researchability:

• Ensure that the variables involved can be accurately measured or observed using existing tools or methods.
• Design hypotheses that are testable within the constraints of time, resources, and ethical considerations.
• Consider the practical aspects of data collection, including access to participants, media content, or archival resources.

Crafting research questions and hypotheses that are clear and precise, relevant to the field, and amenable to empirical investigation is crucial for conducting impactful research in mass communications. These criteria not only guide the research process but also enhance the study’s validity, reliability, and contribution to the field, fostering a deeper understanding of the complex dynamics that shape media and communication in society.

7.12 Common Mistakes to Avoid in Formulating Research Questions and Hypotheses

When embarking on a research project, especially in a field as dynamic as mass communications, the formulation of research questions and hypotheses is a critical step that sets the stage for the entire study. However, researchers, particularly those new to the field, may encounter pitfalls that can compromise the clarity, relevance, and feasibility of their research. This section highlights common mistakes to avoid in the formulation process, ensuring that research questions and hypotheses are both robust and actionable.

Formulating Questions and Hypotheses That Are Too Broad or Vague

Issue: Broad or vague questions and hypotheses lack specificity and focus, making it difficult to define the scope of the study or determine the appropriate methodology for investigation.

Impact: They can lead to an unwieldy research project with diffuse objectives, posing challenges in data collection, analysis, and interpretation of findings.

Avoidance Strategy: Narrow down the research topic by focusing on specific aspects, populations, or contexts. Use the literature review to identify gaps and refine the research focus to a manageable scope.

Confusing Research Questions with Interview or Survey Questions

Issue: There is a distinction between overarching research questions that guide a study and the specific questions posed in interviews or surveys. Confusing the two can lead to a misalignment between the study’s objectives and the data collection process.

Impact: This confusion can result in collecting data that do not effectively address the research questions, undermining the study’s ability to generate meaningful insights.

Avoidance Strategy: Clearly delineate between the broad research questions that frame your study and the specific items or prompts used in data collection instruments. Ensure that each interview or survey question is directly linked to and serves the purpose of answering the overarching research questions.

Creating Untestable Hypotheses

Issue: Hypotheses that are not empirically testable, either due to the abstract nature of the constructs involved or the lack of available methods for measurement, pose significant challenges to the research process.

Impact: Untestable hypotheses cannot be substantiated or refuted through empirical evidence, limiting the study’s contribution to the field and its scientific merit.

Avoidance Strategy: Ensure that all variables in the hypothesis can be measured or manipulated with existing research methods. Operationalize abstract concepts clearly and consider the feasibility of empirical testing during the hypothesis formulation stage.

7.13 Best Practices for Robust Formulation

Alignment with Theoretical Frameworks: Ground your research questions and hypotheses within established theories or models in mass communications, ensuring they contribute to the broader academic dialogue.

Consultation with Peers and Mentors: Engage in discussions with peers, mentors, or experts in the field to refine your research questions and hypotheses, leveraging their insights to avoid common pitfalls.

Pilot Testing: Consider conducting a pilot study or preliminary analysis to test the feasibility of your research questions and hypotheses, allowing for adjustments before the full-scale study.

By avoiding these common mistakes and adhering to best practices, researchers can formulate research questions and hypotheses that are clear, focused, and empirically testable. This careful preparation enhances the quality and impact of research in mass communications, contributing valuable insights into the complex interplay between media, technology, and society.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Dissertation

Theoretical Framework Example for a Thesis or Dissertation

Published on October 14, 2015 by Sarah Vinz . Revised on July 18, 2023 by Tegan George.

Your theoretical framework defines the key concepts in your research, suggests relationships between them, and discusses relevant theories based on your literature review .

A strong theoretical framework gives your research direction. It allows you to convincingly interpret, explain, and generalize from your findings and show the relevance of your thesis or dissertation topic in your field.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

Sample problem statement and research questions, sample theoretical framework, your theoretical framework, other interesting articles.

Your theoretical framework is based on:

• Your problem statement
• Your research questions
• Your literature review

A new boutique downtown is struggling with the fact that many of their online customers do not return to make subsequent purchases. This is a big issue for the otherwise fast-growing store.Management wants to increase customer loyalty. They believe that improved customer satisfaction will play a major role in achieving their goal of increased return customers.

To investigate this problem, you have zeroed in on the following problem statement, objective, and research questions:

• Problem : Many online customers do not return to make subsequent purchases.
• Objective : To increase the quantity of return customers.
• Research question : How can the satisfaction of the boutique’s online customers be improved in order to increase the quantity of return customers?

The concepts of “customer loyalty” and “customer satisfaction” are clearly central to this study, along with their relationship to the likelihood that a customer will return. Your theoretical framework should define these concepts and discuss theories about the relationship between these variables.

Some sub-questions could include:

• What is the relationship between customer loyalty and customer satisfaction?
• How satisfied and loyal are the boutique’s online customers currently?
• What factors affect the satisfaction and loyalty of the boutique’s online customers?

As the concepts of “loyalty” and “customer satisfaction” play a major role in the investigation and will later be measured, they are essential concepts to define within your theoretical framework .

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Below is a simplified example showing how you can describe and compare theories in your thesis or dissertation . In this example, we focus on the concept of customer satisfaction introduced above.

Customer satisfaction

Thomassen (2003, p. 69) defines customer satisfaction as “the perception of the customer as a result of consciously or unconsciously comparing their experiences with their expectations.” Kotler & Keller (2008, p. 80) build on this definition, stating that customer satisfaction is determined by “the degree to which someone is happy or disappointed with the observed performance of a product in relation to his or her expectations.”

Performance that is below expectations leads to a dissatisfied customer, while performance that satisfies expectations produces satisfied customers (Kotler & Keller, 2003, p. 80).

The definition of Zeithaml and Bitner (2003, p. 86) is slightly different from that of Thomassen. They posit that “satisfaction is the consumer fulfillment response. It is a judgement that a product or service feature, or the product of service itself, provides a pleasurable level of consumption-related fulfillment.” Zeithaml and Bitner’s emphasis is thus on obtaining a certain satisfaction in relation to purchasing.

Thomassen’s definition is the most relevant to the aims of this study, given the emphasis it places on unconscious perception. Although Zeithaml and Bitner, like Thomassen, say that customer satisfaction is a reaction to the experience gained, there is no distinction between conscious and unconscious comparisons in their definition.

The boutique claims in its mission statement that it wants to sell not only a product, but also a feeling. As a result, unconscious comparison will play an important role in the satisfaction of its customers. Thomassen’s definition is therefore more relevant.

Thomassen’s Customer Satisfaction Model

According to Thomassen, both the so-called “value proposition” and other influences have an impact on final customer satisfaction. In his satisfaction model (Fig. 1), Thomassen shows that word-of-mouth, personal needs, past experiences, and marketing and public relations determine customers’ needs and expectations.

These factors are compared to their experiences, with the interplay between expectations and experiences determining a customer’s satisfaction level. Thomassen’s model is important for this study as it allows us to determine both the extent to which the boutique’s customers are satisfied, as well as where improvements can be made.

Figure 1 Customer satisfaction creation 

Of course, you could analyze the concepts more thoroughly and compare additional definitions to each other. You could also discuss the theories and ideas of key authors in greater detail and provide several models to illustrate different concepts.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Anchoring bias
• Halo effect
• The Baader–Meinhof phenomenon
• The placebo effect
• Nonresponse bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Vinz, S. (2023, July 18). Theoretical Framework Example for a Thesis or Dissertation. Scribbr. Retrieved September 8, 2024, from https://www.scribbr.com/dissertation/theoretical-framework-example/

Is this article helpful?

Sarah's academic background includes a Master of Arts in English, a Master of International Affairs degree, and a Bachelor of Arts in Political Science. She loves the challenge of finding the perfect formulation or wording and derives much satisfaction from helping students take their academic writing up a notch.

Other students also liked

What is a theoretical framework | guide to organizing, how to write a literature review | guide, examples, & templates, what is a research methodology | steps & tips, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

How to Write a Dissertation: A Beginner’s Guide 

Back to School – Lock-in All Access Pack for a Year at the Best Price

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

Brief and easily digested

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right approach

The process of choosing the right research design can put ourselves at the crossroads of…

• Industry News

COPE Forum Discussion Highlights Challenges and Urges Clarity in Institutional Authorship Standards

The COPE forum discussion held in December 2023 initiated with a fundamental question — is…

• Career Corner

Unlocking the Power of Networking in Academic Conferences

Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

How to Design Effective Research Questionnaires for Robust Findings

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• Publishing Research
• AI in Academia
• Promoting Research
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

• Privacy Policy

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Dissertation Methodology – Structure, Example...

Research Summary – Structure, Examples and...

Research Report – Example, Writing Guide and...

Tables in Research Paper – Types, Creating Guide...

Theoretical Framework – Types, Examples and...

Thesis Format – Templates and Samples

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

THEORETICAL AND CONCEPTUAL FRAMEWORKS IN RESEARCH: CONCEPTUAL CLARIFICATION

• September 2023
• European Chemical Bulletin 12(12):2103-2117
• 12(12):2103-2117

• Obafemi Awolowo University

• Islamic University in Uganda (IUIU)
• This person is not on ResearchGate, or hasn't claimed this research yet.

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Asiya Khattak
• Taskeen Haider
• Bisma Javed
• Susilawati Latif

• Fathu Rahman
• Buyisani Dube

• Joe Adu-Agyem

• W. P. J. Fisher

• Matthew B Miles
• Michael A. Huberman

• Judith Preissle Goetz

• Matthew Riggan
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Determinants of business resilience: Investigating the roles of business agility, digitalization, and environmental hostility during the COVID-19 pandemic

• Published: 07 September 2024

Cite this article

• Jamal Maalouf 1 ,
• Lynn Chahine   ORCID: orcid.org/0000-0001-5260-7208 2 ,
• Amine Abi Aad 3 &
• Kevin Sevag Kertechian 4  

Dynamic capabilities research has demonstrated that businesses must adapt to changes in order to survive. The COVID-19 pandemic has put firm resilience to the test. The present study uses the dynamic-capabilities theoretical framework to shed light on the determinants of business resilience. Results from a sample of 243 full-time managers and business owners demonstrate that more agile businesses are more likely to be resilient. In addition, environmental hostility positively moderates the relationship between agility and resilience, as it was stronger with a high environmental hostility and weaker with a low environmental hostility. Digitalization also showed a positive relationship with resilience. Likewise, evidence does not support the hypothesis that digitalization moderates the positive relationship between agility and resilience, i.e., that as engagement increases, the relationship becomes stronger, possibly due to the burnout effect of digitalization. Finally, implications and future research opportunities are presented.

Les recherches portant sur les capacités dynamiques mettent en lumière la nécessité pour les entreprises de s'adapter au changement pour assurer leur survie. En outre, la résilience des entreprises a été mise à l'épreuve par la pandémie de COVID-19. Cette étude se base sur le cadre théorique des capacités dynamiques afin d'analyser les facteurs influençant la résilience des entreprises. Les résultats obtenus auprès d'un échantillon de 243 gestionnaires à temps plein et propriétaires d'entreprises indiquent une corrélation entre l'agilité des entreprises et leur résilience. De surcroît, les résultats soulignent l'impact positif et modérateur de l'hostilité de l'environnement sur la relation entre l'agilité et la résilience, cette relation étant renforcée en cas d'hostilité élevée et atténuée en cas d'hostilité moindre. Par ailleurs, la digitalisation est également associée de manière positive à la résilience des entreprises. Cependant, l'idée selon laquelle la digitalisation pourrait modérer positivement la relation entre l'agilité et la résilience, en renforçant cette dernière dans le cas d'un fort engagement, n'a pas été confirmée en raison de l'épuisement induit par la digitalisation. Enfin, des implications pratiques et des orientations pour de futures recherches sont suggérées.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Abi Aad A, Combs JG (2021) Choosing between the formal and informal economy: how do managers decide? Cross Cult Strat Manag 28(3):679–701. https://doi.org/10.1108/CCSM-07-2020-0140

Article   Google Scholar  

Abouzeid M, Habib RR, Jabbour S, Mokdad AH, Nuwayhid I (2020) Lebanon's humanitarian crisis escalates after the Beirut blast. The Lancet 396(10260):1380–1382

Agénor P (2002) Business cycles, economic crises, and the poor. J Policy Reform 5(3):145–160. https://doi.org/10.1080/1384128021000066080

Akgün AE, Keskin H (2014) Organisational resilience capacity and firm product innovativeness and performance. Int J Prod Res 52(23):6918–6937. https://doi.org/10.1080/00207543.2014.910624

Alekseev G, Amer S, Gopal M, Kuchler T, Schneider JW, Stroebel J, Wernerfelt N (2023) The effects of COVID-19 on US small businesses: evidence from owners, managers, and employees. Manage Sci 69(1):7–24

Amankwah-Amoah J, Khan Z, Wood G (2021) COVID-19 and business failures: the paradoxes of experience, scale, and scope for theory and practice. Eur Manag J 39(2):179–184. https://doi.org/10.1016/j.emj.2020.09.002

Ashrafi N, Xu P, Sathasivam M, Kuilboer JP, Koelher W, Heimann D, Waage F (2005) A framework for implementing business agility through knowledge management systems. In: Seventh IEEE International Conference on E-Commerce Technology Workshops. IEEE, pp 116–121 

Ates A, Bititci U (2011) Change process: a key enabler for building resilient SMEs. Int J Prod Res 49(18):5601–5618. https://doi.org/10.1080/00207543.2011.563825

Backman M, Karlsson C (2020) Age of managers and employees – firm survival. J Econ Ageing 15:100215. https://doi.org/10.1016/j.jeoa.2019.100215

Baltar F, Brunet I (2012) Social research 2.0: virtual snowball sampling method using Facebook. Int Res 22(1):57–74. https://doi.org/10.1108/10662241211199960

Bartik AW, Bertrand M, Cullen Z, Glaeser EL, Luca M, Stanton C (2020) The impact of COVID-19 on small business outcomes and expectations. Proc Natl Acad Sci 117(30):17656–17666. https://doi.org/10.1073/pnas.2006991117

Bofinger P, Dullien S, Felbermayr G, Fuest C, Hüther M, Südekum J, Weder di Mauro B (2020) Wirtschaftliche Implikationen der Corona-Krise und wirtschaftspolitische Maßnahmen [Economic Implications of the Corona Crisis and Economic Policy Measures]. Wirtschaftsdienst 100(4):259–265. https://doi.org/10.1007/s10273-020-2628-0

Bourgeois LJ III, Eisenhardt KM (1988) Strategic decision processes in high velocity environments: Four cases in the microcomputer industry. Manage Sci 34(7):816–835. https://doi.org/10.1287/mnsc.34.7.816

Brammer S, Branicki L, Linnenluecke M (2020) COVID-19, societalization and the future of business in society. Acad Manag Perspect 34(4):493. https://doi.org/10.5465/amp.2019.0053

Broccardo L, Zicari A, Jabeen F, Bhatti ZA (2023) How digitalization supports a sustainable business model: a literature review. Technol Forecast Soc Chang 187:122146. https://doi.org/10.1016/j.techfore.2022.122146

Brougham D, Haar J (2018) Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. J Manage Organ 24(2):239–257

Calantone RJ, Schmidt JB, Di Benedetto CA (1997) New product activities and performance: the moderating role of environmental hostility. J Prod Innov Manag 14(3):179–189. https://doi.org/10.1016/S0737-6782(97)00004-0

Camarinha-Matos LM (2014) Collaborative networks: A mechanism for enterprise agility and resilience. In Enterprise Interoperability VI: Interoperability for Agility, Resilience and Plasticity of Collaborations (pp. 3-11). Springer International Publishing. https://doi.org/10.1007/978-3-319-04948-9_1

Chatterjee S, Chaudhuri R, Vrontis D, Maalaoui A (2023) Internationalization of family business and its performance: examining the moderating role of digitalization and international networking capability. Rev Manag Sci 17(7):2443-2470

Ciacci A,  Penco L (2023) Business model innovation: harnessing big data analytics and digital transformation in hostile environments. J Small Bus Enterp D 31(8):22–46

Ciacci A, Penco L (2024) Business model innovation: harnessing big data analytics and digital transformation in hostile environments. J Small Bus Enterp Dev 31(8):22–46. https://doi.org/10.1108/JSBED-10-2022-0424

Conforto EC, Amaral DC, Da Silva SL, Di Felippo A, Kamikawachi DSL (2016) The agility construct on project management theory.  Int J Proj Manag 34(4):660-674

Cole G (2015) Blending resilience and sustainability: business success in an unpredictable world. Strateg Dir 31(1):6–8. https://doi.org/10.1108/SD-10-2014-0153

Covin JG, Slevin DP (1989) Strategic management of small firms in hostile and benign environments. Strateg Manag J 10(1):75–87. https://doi.org/10.1002/smj.4250100107

Covin JG, Slevin DP (1991) A conceptual model of entrepreneurship as firm behavior. Entrep Theory Pract 16(1):7–26. https://doi.org/10.1177/104225879101600102

Delardas O, Kechagias KS, Pontikos PN, Giannos P (2022) Socio-economic impacts and challenges of the coronavirus pandemic (COVID-19): an updated review. Sustainability 14(15):9699

DesJardine MR, Bansal P (2013) The value of business sustainability and resilience during the global financial crisis. Acad Manag Proc 2013(1):16501. https://doi.org/10.5465/ambpp.2013.16501abstract

Devi S (2020) Lebanon faces humanitarian emergency after the blast. The Lancet 396(10249):456. https://doi.org/10.1016/S0140-6736(20)31750-5

Dyba W, Di Maria E (2024) Assessment and support of the digitalization of businesses in Europe during and after the COVID-19 pandemic. Reg Sci Policy Pract 16(1):12717. https://doi.org/10.1111/rsp3.12717

ECOSOC (2020) The sustainable development goals report 2020. Retrieved October 16, 2020, from https://unstats.un.org/sdgs/report/2020/

e Cunha MP, Gomes E, Mellahi K, Miner AS, Rego A (2020) Strategic agility through improvisational capabilities: Implications for a paradox-sensitive HRM. Hum Resour Manag Rev 30(1):100695

Eisenhardt KM, Martin JA (2000) Dynamic capabilities: what are they? Strateg Manag J 21(10):1105–1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11%3c1105::AID-SMJ133%3e3.0.CO;2-E

Elliott D, Swartz E, Herbane B (2010) Business continuity management: a crisis management approach, 1st edn. Routledge, UK. https://doi.org/10.4324/9780203866337

Book   Google Scholar  

Fairlie RW (2022) The impacts of COVID-19 on racial inequality in business earnings (No. w30532). National Bureau of Economic Research

Fairlie RW, Fossen F (2021) Did the $660 billion paycheck protection program and $220 billion economic injury disaster loan program get disbursed to minority communities in the early stages of COVID-19? (No. w28321). National Bureau of Economic Research

Fakih, Ali and Makdissi, Paul and Marrouch, Walid and Tabri, Rami and Yazbeck, Myra, Confidence in public institutions and the run up to the October 2019 uprising in Lebanon. IZA Discussion Paper No. 13104. https://ssrn.com/abstract=3568309 . Accessed Dec 2021

Florio VD (2013) On the constituent attributes of software and organizational resilience. Interdisc Sci Rev 38(2):122–148. https://doi.org/10.1179/0308018813Z.00000000040

Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation?. Technol Forecast Soc Change 114:254–280

Giantari I, Yasa N, Suprasto H, Rahmayanti P (2022) The role of digital marketing in mediating the effect of the COVID-19 pandemic and the intensity of competition on business performance. Int J Data Netw Sci 6(1):217–232

Gielnik MM, Zacher H, Frese M (2012) Focus on opportunities as a mediator of the relationship between business owners’ age and venture growth. J Bus Ventur 27(1):127–142. https://doi.org/10.1016/j.jbusvent.2010.05.002

Gobble MM (2018) Digitalization, digitization, and innovation. Res Technol Manage 61(4):56-59

Goglio-Primard K, Simon L, Cohendet P, Aharonson BS, Wenger-Trayner E (2020) Managing with communities for innovation, agility, and resilience. Eur Manag J 38(5):673–675. https://doi.org/10.1016/j.emj.2020.08.003

Goldman SL, Nagel RN, Preiss K (1995) Agile competitors and virtual organizations: strategies for enriching the customer. Van Nostrand Reinhold

Google Scholar  

Gómez SM, Mendoza OEO, Ramírez J, Olivas-Luján MR (2020) Stress and myths related to the COVID-19 pandemic’s effects on remote work. Manag Res 18(4):401–420. https://doi.org/10.1108/MRJIAM-06-2020-1065

Goodhart CA, Tsomocos DP, Wang X (2023) Support for small businesses amid COVID-19. Economica 90(358):612–652. https://doi.org/10.1111/ecca.12460

Guechati I, Chami M (2022) Lebanon, economic and financial crises, reasons for collapse. Revue Française D’economie Et De Gestion 3(6):276–291

Heidt L, Gauger F, Pfnür A (2023) Work from home success: agile work characteristics and the mediating effect of supportive HRM. RMS 17(6):2139–2164. https://doi.org/10.1007/s11846-022-00545-5

Heisterberg RJ, Verma A (2014) Creating business agility: how convergence of cloud, social, mobile, video, and big data enables competitive advantage. Wiley, Hoboken, N.J

Herbane B (2019) Rethinking organizational resilience and strategic renewal in SMEs. Entrep Reg Dev 31(5–6):476–495. https://doi.org/10.1080/08985626.2018.1541594

Hillmann J, Guenther E (2021) Organizational resilience: a valuable construct for management research? Int J Manag Rev: IJMR 23(1):7–44. https://doi.org/10.1111/ijmr.12239

Hitt MA, Holmes RM, Mistry S (2023) Agility, strategies, dynamic capabilities, stakeholders, and strategic leadership in the new normal environment. In Senior Leadership Teams and the Agile Organization (pp. 336-362). Routledge

Holopainen M, Ukko J, Saunila M (2022) Managing the strategic readiness of industrial companies for digital operations. Digital Business 2(2):100039. https://doi.org/10.1016/j.digbus.2022.100039

ILO Monitor (2020) COVID-19 and the world of work. Third edition. Retrieved October 18, 2020, from http://www.cnt-nar.be/DOSSIERS/Covid-19/ILO-2020-04-29-Covid-19-and-the-world-of-work-(03)-EN.pdf

Isip MI, Baconguis R, Depositario DP, Quimbo MA, Paunlagui M (2023) How environmental hostility, entrepreneurial orientation and dynamic capabilities are manifested to influence above-average export performance during the COVID-19 pandemic. Contin Resilience Rev 5(2):135–157. https://doi.org/10.1108/CRR-10-2022-0025

Ivory SB, Brooks SB (2018) Managing corporate sustainability with a paradoxical lens: lessons from strategic agility. J Bus Ethics 148(2):347–361. https://doi.org/10.1007/s10551-017-3583-6

Jameson DA (2009) Economic crises and financial disasters: the role of business communication. J Bus Commun 46(4):499–509. https://doi.org/10.1177/0021943609338667

Joseph E, Dhanabhakyam MM (2022) Role of Digitalization Post-Pandemic for Development of SMEs. In Research anthology on business continuity and navigating times of crisis (pp. 727-747). IGI Global

Kertechian KS, El-Farr H (2024) Dissecting the paradox of progress: the socioeconomic implications of artificial intelligence. IntechOpen. https://doi.org/10.5772/intechopen.1004872

Khandwalla PN (1976) Some top management styles, their context and performance. Organ Adm Sci 7(4):21–51

Khelil N, Khiari S, de Caen Basse-Normandie IAE (2013) Pour une prise en compte du caractère multiforme de l’échec entrepreneurial en vue d’un meilleur accompagnement des jeunes créateurs d’entreprises en difficultés. Document de travail, Université de Caen Normandie

Kreiser PM, Anderson BS, Kuratko DF, Marino LD (2020) Entrepreneurial orientation and environmental hostility: a threat rigidity perspective. Entrep Theory Pract 44(6):1174–1198. https://doi.org/10.1177/1042258719891389

Lee Y, Zhuang Y, Joo M, Bae TJ (2020) Revisiting Covin and Slevin (1989): replication and extension of the relationship between entrepreneurial orientation and firm performance. J Bus Ventur Insights 12:e00144. https://doi.org/10.1016/j.jbvi.2019.e00144

Levallet N, Chan Y (2022) Uncovering a new form of digitally-enabled agility: an improvisational perspective. Eur J Inform Syst 31(6):681–708

Liao Y, Deschamps F, Loures EDFR, Ramos LFP (2017) Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International journal of production research 55(12):3609-3629

Li Z, Rasool S, Cavus MF, Shahid W (2024) Sustaining the future: how green capabilities and digitalization drive sustainability in modern business. Heliyon 10(1):e24158. https://doi.org/10.1016/j.heliyon.2024.e24158

Linnenluecke MK (2017) Resilience in business and management research: a review of influential publications and a research agenda: resilience in business and management research. Int J Manag Rev : IJMR 19(1):4–30. https://doi.org/10.1111/ijmr.12076

Madhok A, Marques R (2014) Towards an action-based perspective on firm competitiveness. BRQ Bus Res Q 17(2):77–81. https://doi.org/10.1016/j.brq.2014.03.002

Makdissi P, Seif Edine M (2020) Is the elimination of food subsidies the right policy to address Lebanon’s public finance crisis? Review of Middle East Economics and Finance 16(2):20200016. https://doi.org/10.1515/rmeef-2020-0016

Martins A (2023) Dynamic capabilities and SME performance in the COVID-19 era: the moderating effect of digitalization. Asia-Pacific J Bus Adm 15(2): 188-202

Maslach C, Leiter MP (2008) The truth about burnout: how organizations cause personal stress and what to do about it. Jossey & Bass

Menéndez Blanco JM, Montes Botella JL (2016) What contributes to adaptive company resilience? A conceptual and practical approach. Dev Learn Organ 30(4):17–20. https://doi.org/10.1108/DLO-10-2015-0080

Michie J (2020) The covid-19 crisis – and the future of the economy and economics. Int Rev Appl Econ 34(3):301–303. https://doi.org/10.1080/02692171.2020.1756040

Miceli A, Hagen B, Riccardi MP, Sotti F, Settembre-Blundo D (2021) Thriving, not just surviving in changing times: How sustainability, agility and digitalization intertwine with organizational resilience. Sustainability 13(4):2052

Miles MP, Arnold DR, Thompson DL (1993) The interrelationship between environmental hostility and entrepreneurial orientation. J Appl Bus Res 9(4):12–24. https://doi.org/10.19030/jabr.v9i4.5984

Miller D, Friesen PH (1978) Archetypes of strategy formulation. Manage Sci 24(9):921–933. https://doi.org/10.1287/mnsc.24.9.921

Miller D, Friesen PH (1983) Strategy-making and environment: the third link. Strateg Manag J 4(3):221–235. https://doi.org/10.1002/smj.4250040304

Mintzberg H (1979) Patterns in strategy formation. Int Stud Manag Organ 9(3):67–86

MOPH (2020) Epidemiological surveillance program of COVID-19. Retrieved October 18, 2020, from https://www.moph.gov.lb/en/Pages/2/24870/novel -coronaviru s-2019

Moorman C, Miner A (1998) The convergence of planning and execution: Improvisation in new product development. J Mark 62:1–20

Motwani J, Katatria A (2024) Organization agility: a literature review and research agenda, International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPPM-07-2023-0383

Munteanu A, Bibu N, Nastase M, Cristache N, Matis C (2020) Analysis of practices to increase the workforce agility and to develop a sustainable and competitive business. Sustainability 12(9):3545. https://doi.org/10.3390/su12093545

Napier E, Liu SY, Liu J (2024) Adaptive strength: unveiling a multilevel dynamic process model for organizational resilience. J Bus Res 171:114334

Nielsen BB, Wechtler H, Linglin Z (2023) Disasters and international business: Insights and recommendations from a systematic review. J World Bus 58(4):101458. https://doi.org/10.1016/j.jwb.2023.101458

Nivetha P, Sudhamathi S (2021) Survival strategies for businesses during Covid-19 lockdown. Shanlax Int J Manag 8(3):79–82

Nuwayhid I, Zurayk H (2019) The political determinants of health and well-being in the Lebanese uprising. Lancet 394(10213):1974–1975. https://doi.org/10.1016/S0140-6736(19)32907-1

Oldekop JA, Horner R, Hulme D, Adhikari R, Agarwal B, Alford M, Bakewell O, Banks N, Barrientos S, Bastia T, Bebbington AJ, Das U, Dimova R, Duncombe R, Enns C, Fielding D, Foster C, Foster T, Frederiksen T, Gao P, Zhang YF (2020) COVID-19 and the case for global development. World Development 134:105044. https://doi.org/10.1016/j.worlddev.2020.105044

Omrane A, Kammoun A, Seaman C (2018) Entrepreneurial burnout: causes, consequences, and way out. FIIB Business Rev 7(1):28–42. https://doi.org/10.1177/2319714518767805

Orvos J, SpringerLink (Online service) (2019) Achieving business agility: strategies for becoming pivot ready in a digital world. APress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3855-4

Pacheco I (2020) Shopify, suddenly worth $117 billion, is one of the biggest pandemic winners. The Wall Street Journal. https://www.wsj.com/articles/shopify-suddenly-worth-117-billion-is-one-of-the-biggest-pandemic-winners-11599557400reflink=desktopwebshare_permalink . Accessed December 2021

Pal R, Torstensson H, Mattila H (2014) Antecedents of organizational resilience in economic crises—an empirical study of Swedish textile and clothing SMEs. Int J Prod Econ 147:410–428. https://doi.org/10.1016/j.ijpe.2013.02.031

Peñarroya-Farell M, Miralles F (2022) Business model adaptation to the COVID-19 crisis: Strategic response of the Spanish cultural and creative firms. Journal of Open Innovation: Technol Mark Complex 8(1):39

Peñarroya-Farell M, Miralles F (2021) Business model dynamics from interaction with open innovation. Journal of Open Innovation: Technol Mark Complex 7(1):81

Pla-Barber J, Villar C, Ghauri P (2020) The internationalization of SMEs: building models for long-term development. BRQ Bus Res Q 23(2):88–90. https://doi.org/10.1177/2340944420916330

Polinkevych O, Khovrak I, Trynchuk V, Klapkiv Y, Volynets I (2021) Business risk management in times of crises and pandemics. Montenegrin J Econ 17(3):99–110. https://doi.org/10.14254/1800-5845/2021.17-3.8

Prayag G, Muskat B, Dassanayake C (2024) Leading for resilience: fostering employee and organizational resilience in tourism firms. J Travel Res 63(3):659–680. https://doi.org/10.1177/00472875231164984

Raj M, Sundararajan A, You C (2020) COVID-19 and digital resilience: evidence from uber eats (March 22, 2021). NYU Stern School of Business, 2021. https://ssrn.com/abstract=3625638  or  https://doi.org/10.2139/ssrn.3625638

Rani A, Salanke P (2023) A bibliometric analysis on business and management research during COVID-19 pandemic: trends and prospects. IMIB J Innov Manag 1(2):233–246. https://doi.org/10.1177/ijim.221148834

Ravichandran T (2018) Exploring the relationships between IT competence, innovation capacity and organizational agility. J Strateg Inf Syst 27(1):22–42. https://doi.org/10.1016/j.jsis.2017.07.002

Reis J, Amorim M, Melão N, Cohen Y, Rodrigues M (2020) Digitalization: a literature review and research agenda. In: Proceedings on 25th International joint conference on industrial engineering and operations management–IJCIEOM: the next generation of production and service systems vol 25. Springer International Publishing, pp 443–456

Ricciardi F, Zardini A, Rossignoli C (2016) Organizational dynamism and adaptive business model innovation: the triple paradox configuration. J Bus Res 69(11):5487–5493. https://doi.org/10.1016/j.jbusres.2016.04.154

Ronzon T, Buck J, Eckstein J (2019) Making companies nimble-from software agility to business agility: a conversation with the authors [insights]. IEEE Softw 36(1):79–85. https://doi.org/10.1109/ms.2018.2874321

Ross J (2017) Speculative method in digital education research. Learn Media Technol 42(2):214–229

Russell AR, Frisone M, Frumkin P (2023) Layoffs during a pandemic results from an experiment on the management practices of nonprofit organizations and business firms. Nonprofit Manag Leadership 34(4):825. https://doi.org/10.1002/nml.21593

Samantha G (2018) The impact of natural disasters on micro, small and medium enterprises (MSMEs): a case study on 2016 flood event in western Sri Lanka. Procedia Engineering 212:744–751. https://doi.org/10.1016/j.proeng.2018.01.096

Sambamurthy V, Bharadwaj A, Grover V (2003) Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS Q 27(2):237–263. https://doi.org/10.2307/30036530

Schroeder P, Anggraeni K, Weber U (2019) The relevance of circular economy practices to the sustainable development goals. J Ind Ecol 23(1):77–95

Seetharaman P (2020) Business models shifts: impact of covid-19. Int J Inf Manage 54:102173. https://doi.org/10.1016/j.ijinfomgt.2020.102173

Shafi M, Liu J, Ren W (2020) Impact of COVID-19 pandemic on micro, small, and medium-sized enterprises operating in Pakistan. Res Glob 2:100018. https://doi.org/10.1016/j.resglo.2020.100018

Shajrawi A, Aburub F (2023) Impact of ERP usage on service differentiation: role of mediating effect of organizational agility. Arab Gulf J Sci Res 41(3):359–375. https://doi.org/10.1108/AGJSR-06-2022-0085

Silverman BS, Nickerson JA, Freeman J (1997) Profitability, transactional alignment, and organizational mortality in the US trucking industry. Strateg Manag J 18(S1):31–52. https://doi.org/10.1002/(SICI)1097-0266(199707)18:1+%3c31::AID-SMJ920%3e3.0.CO;2-S

Singh AK, Vinodh S (2017) Modeling and performance evaluation of agility coupled with sustainability for business planning. J Manag Dev 36(1):109–128. https://doi.org/10.1108/JMD-10-2014-0140

Stone EF, Hollenbeck JR (1989) Clarifying some controversial issues surrounding statistical procedures for detecting moderator variables: empirical evidence and related matters. J Appl Psychol 74(1):3–10. https://doi.org/10.1037/0021-9010.74.1.3

Sutton RI, Eisenhardt KM, Jucker JV (1986) Managing organizational decline: lessons from Atari. Organ Dyn 14(4):17–29. https://doi.org/10.1016/0090-2616(86)90041-0

Tallon PP, Pinsonneault A (2011) Competing perspectives on the link between strategic information technology alignment and organizational agility: insights from a mediation model. MIS Q 35(2):463–486. https://doi.org/10.2307/23044052

Teece DJ (2012) Dynamic capabilities: routines versus entrepreneurial action. J Manage Stud 49(8):1395–1401. https://doi.org/10.1111/j.1467-6486.2012.01080.x

Teece DJ (2015) Dynamic capabilities and entrepreneurial management in large organizations: toward a theory of the (entrepreneurial) firm. Eur Econ Rev 86(2016):202–216. https://doi.org/10.1016/j.euroecorev.2015.11.006

Teece D, Peteraf M, Leih S (2016) Dynamic capabilities and organizational agility: risk, uncertainty, and strategy in the innovation economy. Calif Manage Rev 58(4):13–35. https://doi.org/10.1525/cmr.2016.58.4.13

Verma S, Gustafsson A (2020) Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J Bus Res 118:253–261. https://doi.org/10.1016/j.jbusres.2020.06.057

Villar EB, Miralles F (2021) Purpose‐driven improvisation during organisational shocks: case narrative of three critical organisations and Typhoon Haiyan. Disasters 45(2):477–497

Wall T, Bellamy L (2019) Redressing small firm resilience exploring owner-manager resources for resilience. Int J Org Anal (2005) 27(2):269–288. https://doi.org/10.1108/IJOA-02-2018-1364

Wang Z, Lin S, Chen Y, Lyulyov O, Pimonenko T (2023) Digitalization effect on business performance: role of business model innovation. Sustainability 15(11):9020. https://doi.org/10.3390/su15119020

Weick KE, Sutcliffe KM, Obstfeld D (1999) Organizing for high reliability: processes of collective mindfulness. Res Org Behav 21:81–123

Welch S (1975) Sampling by referral in a dispersed population. Public Opinion Q 39(2):237-245. http://www.jstor.org/stable/2748151

Wenzel M, Stanske S, Lieberman MB (2020) Strategic responses to crisis. Strateg Manag J 41:7–18. https://doi.org/10.1002/smj.3161

Werner S, Brouthers LE, Brouthers KD (1996) International risk and perceived environmental uncertainty: the dimensionality and internal consistency of miller’s measure. J Int Bus Stud 27(3):571–587. https://doi.org/10.1057/palgrave.jibs.8490144

Whitman ZR, Kachali H, Roger D, Vargo J, Seville E (2013) Short-form version of the benchmark resilience tool (BRT-53). Meas Bus Excell 17(3):3–14. https://doi.org/10.1108/MBE-05-2012-0030

WHO (2020). Coronavirus disease (COVID-19) pandemic. Retrieved October 16, 2020, from https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19 -:~:text=WHO announced COVID-19,on 11 March 2020

WHO (2020) Weekly operational update on COVID-19 - 6 November 2020. Retrieved November 6, 2020, from https://www.who.int/publications/m/item/weekly-operational-update-on-covid-19---6-november-2020

Yunita T, Sasmoko S, Bandur A, Alamsjah F (2023) Organizational ambidexterity: the role of technological capacity and dynamic capabilities in the face of environmental dynamism. Heliyon 9(4):e14817. https://doi.org/10.1016/j.heliyon.2023.e14817

Zahari AI, Manan DIA, Razali FM, Zolkaflil S, Said J (2024) Exploring the viability of remote work for SME. J Open Innov 10(1):100182. https://doi.org/10.1016/j.joitmc.2023.100182

Zahra SA (1993) Environment, corporate entrepreneurship, and financial performance: a taxonomic approach. J Bus Ventur 8(4):319–340. https://doi.org/10.1016/0883-9026(93)90003-N

Zawya (2020) Half of Lebanese businesses could close in 2020: Head of the Beirut Traders Association. Retrieved October 20, 2020, from https://www.zawya.com/mena/en/economy/story/Half_of_Lebanese_businesses_could_close_in_2020_Head_of_the_Beirut_Traders_Association-SNG_177212706/ - :~:text=BEIRUT%3A As many as 50,closures outside of the capital

Zhang Y, Lindell MK, Prater CS (2009) Vulnerability of community businesses to environmental disasters. Disasters 33(1):38–57. https://doi.org/10.1111/j.1467-7717.2008.01061.x

Download references

Author information

Authors and affiliations.

American University of Sharjah, Sharjah, United Arab Emirates

Jamal Maalouf

TBS-Research Center, University of Toulouse Capitole, Toulouse, France

Lynn Chahine

American University of Beirut, Beirut, Lebanon

Amine Abi Aad

ESSCA School of Management, Angers, France

Kevin Sevag Kertechian

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Lynn Chahine .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Summary highlights

Contributions of the paper : The paper sheds light on business resilience, especially during the COVID-19 pandemic, when environmental hostility was exceptionally high. Accordingly, different factors that may influence business resilience, and their impact on operational sustainability, were examined.

Research questions/purpose : To understand whether business agility and adaptability can lead to resilience, and whether environmental hostility and digitalization influence this relationship.

Methodology : Quantitative research supported by SPSS and PROCESS macro to analyze survey data collected from 243 participants, using hierarchical moderated regressions.

Results/findings : The findings suggest that agility was favorably associated with resilience. However, whereas environmental hostility was a positive moderator of this connection, the interaction effect of digitization and agility was not significant.

Limitations : This study relied on referral and snowball sampling in its recruitment of participants and relied on self-reported responses to survey questions.

Practical implications : This study elucidates the importance of digitalization in today’s business world, encouraging managers to adopt and integrate such tools into their work, and to be alert of possible hostile environments where they conduct their work. .

Recommendations for future research : Future studies may expand to a more generalizable sample of participants, and examine other factors that might influence these relationships, such as governmental support and corporate innovativeness.

Digitalization Scale.

Please answer the below 6 items as best fits your firm and based on the following Likert scale:

Our company is able to cope with technological changes easily.

Our company has the resources to operate remotely.

Our employees are technologically friendly.

Our company has made a smooth transition onto online platforms.

Most of our operations are currently online.

We were easily able to digitally adapt to the COVID-19 restrictions.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Maalouf, J., Chahine, L., Abi Aad, A. et al. Determinants of business resilience: Investigating the roles of business agility, digitalization, and environmental hostility during the COVID-19 pandemic. J Int Entrep (2024). https://doi.org/10.1007/s10843-024-00357-6

Download citation

Accepted : 25 June 2024

Published : 07 September 2024

DOI : https://doi.org/10.1007/s10843-024-00357-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Environmental hostility
• Sustainability
• Hostilité environnementale

JEL Classification

• Find a journal
• Publish with us
• Track your research