introduction to research hypothesis

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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 Reference Generator.

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

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

You Might Also Like:

16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

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

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.

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

As a researcher, what do you consider most when choosing an image manipulation detector?

How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

Let ATLAS.ti take you from research question to key insights

Get started with a free trial and see how ATLAS.ti can make the most of your data.

In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

Turn data into evidence for insights with ATLAS.ti

Powerful analysis for your research paper or presentation is at your fingertips starting with a free trial.

• Manuscript Preparation

What is and How to Write a Good Hypothesis in Research?

• 4 minute read
• 314.6K views

Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

• Research Process

Systematic Literature Review or Literature Review?

What is a Problem Statement? [with examples]

You may also like.

Make Hook, Line, and Sinker: The Art of Crafting Engaging Introductions

Can Describing Study Limitations Improve the Quality of Your Paper?

A Guide to Crafting Shorter, Impactful Sentences in Academic Writing

6 Steps to Write an Excellent Discussion in Your Manuscript

How to Write Clear and Crisp Civil Engineering Papers? Here are 5 Key Tips to Consider

The Clear Path to An Impactful Paper: ②

The Essentials of Writing to Communicate Research in Medicine

Changing Lines: Sentence Patterns in Academic Writing

Input your search keywords and press Enter.

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Related Articles

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

• Privacy Policy

Home » Research Paper Introduction – Writing Guide and Examples

Research Paper Introduction – Writing Guide and Examples

Table of Contents

Research Paper Introduction

Research paper introduction is the first section of a research paper that provides an overview of the study, its purpose, and the research question (s) or hypothesis (es) being investigated. It typically includes background information about the topic, a review of previous research in the field, and a statement of the research objectives. The introduction is intended to provide the reader with a clear understanding of the research problem, why it is important, and how the study will contribute to existing knowledge in the field. It also sets the tone for the rest of the paper and helps to establish the author’s credibility and expertise on the subject.

How to Write Research Paper Introduction

Writing an introduction for a research paper can be challenging because it sets the tone for the entire paper. Here are some steps to follow to help you write an effective research paper introduction:

• Start with a hook : Begin your introduction with an attention-grabbing statement, a question, or a surprising fact that will make the reader interested in reading further.
• Provide background information: After the hook, provide background information on the topic. This information should give the reader a general idea of what the topic is about and why it is important.
• State the research problem: Clearly state the research problem or question that the paper addresses. This should be done in a concise and straightforward manner.
• State the research objectives: After stating the research problem, clearly state the research objectives. This will give the reader an idea of what the paper aims to achieve.
• Provide a brief overview of the paper: At the end of the introduction, provide a brief overview of the paper. This should include a summary of the main points that will be discussed in the paper.
• Revise and refine: Finally, revise and refine your introduction to ensure that it is clear, concise, and engaging.

Structure of Research Paper Introduction

The following is a typical structure for a research paper introduction:

• Background Information: This section provides an overview of the topic of the research paper, including relevant background information and any previous research that has been done on the topic. It helps to give the reader a sense of the context for the study.
• Problem Statement: This section identifies the specific problem or issue that the research paper is addressing. It should be clear and concise, and it should articulate the gap in knowledge that the study aims to fill.
• Research Question/Hypothesis : This section states the research question or hypothesis that the study aims to answer. It should be specific and focused, and it should clearly connect to the problem statement.
• Significance of the Study: This section explains why the research is important and what the potential implications of the study are. It should highlight the contribution that the research makes to the field.
• Methodology: This section describes the research methods that were used to conduct the study. It should be detailed enough to allow the reader to understand how the study was conducted and to evaluate the validity of the results.
• Organization of the Paper : This section provides a brief overview of the structure of the research paper. It should give the reader a sense of what to expect in each section of the paper.

Research Paper Introduction Examples

Research Paper Introduction Examples could be:

Example 1: In recent years, the use of artificial intelligence (AI) has become increasingly prevalent in various industries, including healthcare. AI algorithms are being developed to assist with medical diagnoses, treatment recommendations, and patient monitoring. However, as the use of AI in healthcare grows, ethical concerns regarding privacy, bias, and accountability have emerged. This paper aims to explore the ethical implications of AI in healthcare and propose recommendations for addressing these concerns.

Example 2: Climate change is one of the most pressing issues facing our planet today. The increasing concentration of greenhouse gases in the atmosphere has resulted in rising temperatures, changing weather patterns, and other environmental impacts. In this paper, we will review the scientific evidence on climate change, discuss the potential consequences of inaction, and propose solutions for mitigating its effects.

Example 3: The rise of social media has transformed the way we communicate and interact with each other. While social media platforms offer many benefits, including increased connectivity and access to information, they also present numerous challenges. In this paper, we will examine the impact of social media on mental health, privacy, and democracy, and propose solutions for addressing these issues.

Example 4: The use of renewable energy sources has become increasingly important in the face of climate change and environmental degradation. While renewable energy technologies offer many benefits, including reduced greenhouse gas emissions and energy independence, they also present numerous challenges. In this paper, we will assess the current state of renewable energy technology, discuss the economic and political barriers to its adoption, and propose solutions for promoting the widespread use of renewable energy.

Purpose of Research Paper Introduction

The introduction section of a research paper serves several important purposes, including:

• Providing context: The introduction should give readers a general understanding of the topic, including its background, significance, and relevance to the field.
• Presenting the research question or problem: The introduction should clearly state the research question or problem that the paper aims to address. This helps readers understand the purpose of the study and what the author hopes to accomplish.
• Reviewing the literature: The introduction should summarize the current state of knowledge on the topic, highlighting the gaps and limitations in existing research. This shows readers why the study is important and necessary.
• Outlining the scope and objectives of the study: The introduction should describe the scope and objectives of the study, including what aspects of the topic will be covered, what data will be collected, and what methods will be used.
• Previewing the main findings and conclusions : The introduction should provide a brief overview of the main findings and conclusions that the study will present. This helps readers anticipate what they can expect to learn from the paper.

When to Write Research Paper Introduction

The introduction of a research paper is typically written after the research has been conducted and the data has been analyzed. This is because the introduction should provide an overview of the research problem, the purpose of the study, and the research questions or hypotheses that will be investigated.

Once you have a clear understanding of the research problem and the questions that you want to explore, you can begin to write the introduction. It’s important to keep in mind that the introduction should be written in a way that engages the reader and provides a clear rationale for the study. It should also provide context for the research by reviewing relevant literature and explaining how the study fits into the larger field of research.

Advantages of Research Paper Introduction

The introduction of a research paper has several advantages, including:

• Establishing the purpose of the research: The introduction provides an overview of the research problem, question, or hypothesis, and the objectives of the study. This helps to clarify the purpose of the research and provide a roadmap for the reader to follow.
• Providing background information: The introduction also provides background information on the topic, including a review of relevant literature and research. This helps the reader understand the context of the study and how it fits into the broader field of research.
• Demonstrating the significance of the research: The introduction also explains why the research is important and relevant. This helps the reader understand the value of the study and why it is worth reading.
• Setting expectations: The introduction sets the tone for the rest of the paper and prepares the reader for what is to come. This helps the reader understand what to expect and how to approach the paper.
• Grabbing the reader’s attention: A well-written introduction can grab the reader’s attention and make them interested in reading further. This is important because it can help to keep the reader engaged and motivated to read the rest of the paper.
• Creating a strong first impression: The introduction is the first part of the research paper that the reader will see, and it can create a strong first impression. A well-written introduction can make the reader more likely to take the research seriously and view it as credible.
• Establishing the author’s credibility: The introduction can also establish the author’s credibility as a researcher. By providing a clear and thorough overview of the research problem and relevant literature, the author can demonstrate their expertise and knowledge in the field.
• Providing a structure for the paper: The introduction can also provide a structure for the rest of the paper. By outlining the main sections and sub-sections of the paper, the introduction can help the reader navigate the paper and find the information they are looking for.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How to Cite Research Paper – All Formats and...

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Design – Types, Methods and Examples

Research Paper Title – Writing Guide and Example

Research Paper Conclusion – Writing Guide and...

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 4. The Introduction
• 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
• Theoretical Framework
• 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

The introduction leads the reader from a general subject area to a particular topic of inquiry. It establishes the scope, context, and significance of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the research problem supported by a hypothesis or a set of questions, explaining briefly the methodological approach used to examine the research problem, highlighting the potential outcomes your study can reveal, and outlining the remaining structure and organization of the paper.

Key Elements of the Research Proposal. Prepared under the direction of the Superintendent and by the 2010 Curriculum Design and Writing Team. Baltimore County Public Schools.

Importance of a Good Introduction

Think of the introduction as a mental road map that must answer for the reader these four questions:

• What was I studying?
• Why was this topic important to investigate?
• What did we know about this topic before I did this study?
• How will this study advance new knowledge or new ways of understanding?

According to Reyes, there are three overarching goals of a good introduction: 1) ensure that you summarize prior studies about the topic in a manner that lays a foundation for understanding the research problem; 2) explain how your study specifically addresses gaps in the literature, insufficient consideration of the topic, or other deficiency in the literature; and, 3) note the broader theoretical, empirical, and/or policy contributions and implications of your research.

A well-written introduction is important because, quite simply, you never get a second chance to make a good first impression. The opening paragraphs of your paper will provide your readers with their initial impressions about the logic of your argument, your writing style, the overall quality of your research, and, ultimately, the validity of your findings and conclusions. A vague, disorganized, or error-filled introduction will create a negative impression, whereas, a concise, engaging, and well-written introduction will lead your readers to think highly of your analytical skills, your writing style, and your research approach. All introductions should conclude with a brief paragraph that describes the organization of the rest of the paper.

Hirano, Eliana. “Research Article Introductions in English for Specific Purposes: A Comparison between Brazilian, Portuguese, and English.” English for Specific Purposes 28 (October 2009): 240-250; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide. Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education.

Structure and Writing Style

I.  Structure and Approach

The introduction is the broad beginning of the paper that answers three important questions for the reader:

• What is this?
• Why should I read it?
• What do you want me to think about / consider doing / react to?

Think of the structure of the introduction as an inverted triangle of information that lays a foundation for understanding the research problem. Organize the information so as to present the more general aspects of the topic early in the introduction, then narrow your analysis to more specific topical information that provides context, finally arriving at your research problem and the rationale for studying it [often written as a series of key questions to be addressed or framed as a hypothesis or set of assumptions to be tested] and, whenever possible, a description of the potential outcomes your study can reveal.

These are general phases associated with writing an introduction: 1.  Establish an area to research by:

• Highlighting the importance of the topic, and/or
• Making general statements about the topic, and/or
• Presenting an overview on current research on the subject.

2.  Identify a research niche by:

• Opposing an existing assumption, and/or
• Revealing a gap in existing research, and/or
• Formulating a research question or problem, and/or
• Continuing a disciplinary tradition.

3.  Place your research within the research niche by:

• Stating the intent of your study,
• Outlining the key characteristics of your study,
• Describing important results, and
• Giving a brief overview of the structure of the paper.

NOTE:   It is often useful to review the introduction late in the writing process. This is appropriate because outcomes are unknown until you've completed the study. After you complete writing the body of the paper, go back and review introductory descriptions of the structure of the paper, the method of data gathering, the reporting and analysis of results, and the conclusion. Reviewing and, if necessary, rewriting the introduction ensures that it correctly matches the overall structure of your final paper.

II.  Delimitations of the Study

Delimitations refer to those characteristics that limit the scope and define the conceptual boundaries of your research . This is determined by the conscious exclusionary and inclusionary decisions you make about how to investigate the research problem. In other words, not only should you tell the reader what it is you are studying and why, but you must also acknowledge why you rejected alternative approaches that could have been used to examine the topic.

Obviously, the first limiting step was the choice of research problem itself. However, implicit are other, related problems that could have been chosen but were rejected. These should be noted in the conclusion of your introduction. For example, a delimitating statement could read, "Although many factors can be understood to impact the likelihood young people will vote, this study will focus on socioeconomic factors related to the need to work full-time while in school." The point is not to document every possible delimiting factor, but to highlight why previously researched issues related to the topic were not addressed.

Examples of delimitating choices would be:

• The key aims and objectives of your study,
• The research questions that you address,
• The variables of interest [i.e., the various factors and features of the phenomenon being studied],
• The method(s) of investigation,
• The time period your study covers, and
• Any relevant alternative theoretical frameworks that could have been adopted.

Review each of these decisions. Not only do you clearly establish what you intend to accomplish in your research, but you should also include a declaration of what the study does not intend to cover. In the latter case, your exclusionary decisions should be based upon criteria understood as, "not interesting"; "not directly relevant"; “too problematic because..."; "not feasible," and the like. Make this reasoning explicit!

NOTE:   Delimitations refer to the initial choices made about the broader, overall design of your study and should not be confused with documenting the limitations of your study discovered after the research has been completed.

ANOTHER NOTE : Do not view delimitating statements as admitting to an inherent failing or shortcoming in your research. They are an accepted element of academic writing intended to keep the reader focused on the research problem by explicitly defining the conceptual boundaries and scope of your study. It addresses any critical questions in the reader's mind of, "Why the hell didn't the author examine this?"

III.  The Narrative Flow

Issues to keep in mind that will help the narrative flow in your introduction :

• Your introduction should clearly identify the subject area of interest . A simple strategy to follow is to use key words from your title in the first few sentences of the introduction. This will help focus the introduction on the topic at the appropriate level and ensures that you get to the subject matter quickly without losing focus, or discussing information that is too general.
• Establish context by providing a brief and balanced review of the pertinent published literature that is available on the subject. The key is to summarize for the reader what is known about the specific research problem before you did your analysis. This part of your introduction should not represent a comprehensive literature review--that comes next. It consists of a general review of the important, foundational research literature [with citations] that establishes a foundation for understanding key elements of the research problem. See the drop-down menu under this tab for " Background Information " regarding types of contexts.
• Clearly state the hypothesis that you investigated . When you are first learning to write in this format it is okay, and actually preferable, to use a past statement like, "The purpose of this study was to...." or "We investigated three possible mechanisms to explain the...."
• Why did you choose this kind of research study or design? Provide a clear statement of the rationale for your approach to the problem studied. This will usually follow your statement of purpose in the last paragraph of the introduction.

IV.  Engaging the Reader

A research problem in the social sciences can come across as dry and uninteresting to anyone unfamiliar with the topic . Therefore, one of the goals of your introduction is to make readers want to read your paper. Here are several strategies you can use to grab the reader's attention:

• Open with a compelling story . Almost all research problems in the social sciences, no matter how obscure or esoteric , are really about the lives of people. Telling a story that humanizes an issue can help illuminate the significance of the problem and help the reader empathize with those affected by the condition being studied.
• Include a strong quotation or a vivid, perhaps unexpected, anecdote . During your review of the literature, make note of any quotes or anecdotes that grab your attention because they can used in your introduction to highlight the research problem in a captivating way.
• Pose a provocative or thought-provoking question . Your research problem should be framed by a set of questions to be addressed or hypotheses to be tested. However, a provocative question can be presented in the beginning of your introduction that challenges an existing assumption or compels the reader to consider an alternative viewpoint that helps establish the significance of your study. 
• Describe a puzzling scenario or incongruity . This involves highlighting an interesting quandary concerning the research problem or describing contradictory findings from prior studies about a topic. Posing what is essentially an unresolved intellectual riddle about the problem can engage the reader's interest in the study.
• Cite a stirring example or case study that illustrates why the research problem is important . Draw upon the findings of others to demonstrate the significance of the problem and to describe how your study builds upon or offers alternatives ways of investigating this prior research.

NOTE:   It is important that you choose only one of the suggested strategies for engaging your readers. This avoids giving an impression that your paper is more flash than substance and does not distract from the substance of your study.

Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. University College Writing Centre. University of Toronto; Introduction. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Introductions. The Writing Center. University of North Carolina; Introductions. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Introductions, Body Paragraphs, and Conclusions for an Argument Paper. The Writing Lab and The OWL. Purdue University; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Resources for Writers: Introduction Strategies. Program in Writing and Humanistic Studies. Massachusetts Institute of Technology; Sharpling, Gerald. Writing an Introduction. Centre for Applied Linguistics, University of Warwick; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks . 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004 ; Writing Your Introduction. Department of English Writing Guide. George Mason University.

Writing Tip

Avoid the "Dictionary" Introduction

Giving the dictionary definition of words related to the research problem may appear appropriate because it is important to define specific terminology that readers may be unfamiliar with. However, anyone can look a word up in the dictionary and a general dictionary is not a particularly authoritative source because it doesn't take into account the context of your topic and doesn't offer particularly detailed information. Also, placed in the context of a particular discipline, a term or concept may have a different meaning than what is found in a general dictionary. If you feel that you must seek out an authoritative definition, use a subject specific dictionary or encyclopedia [e.g., if you are a sociology student, search for dictionaries of sociology]. A good database for obtaining definitive definitions of concepts or terms is Credo Reference .

Saba, Robert. The College Research Paper. Florida International University; Introductions. The Writing Center. University of North Carolina.

Another Writing Tip

When Do I Begin?

A common question asked at the start of any paper is, "Where should I begin?" An equally important question to ask yourself is, "When do I begin?" Research problems in the social sciences rarely rest in isolation from history. Therefore, it is important to lay a foundation for understanding the historical context underpinning the research problem. However, this information should be brief and succinct and begin at a point in time that illustrates the study's overall importance. For example, a study that investigates coffee cultivation and export in West Africa as a key stimulus for local economic growth needs to describe the beginning of exporting coffee in the region and establishing why economic growth is important. You do not need to give a long historical explanation about coffee exports in Africa. If a research problem requires a substantial exploration of the historical context, do this in the literature review section. In your introduction, make note of this as part of the "roadmap" [see below] that you use to describe the organization of your paper.

Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70.

Yet Another Writing Tip

Always End with a Roadmap

The final paragraph or sentences of your introduction should forecast your main arguments and conclusions and provide a brief description of the rest of the paper [the "roadmap"] that let's the reader know where you are going and what to expect. A roadmap is important because it helps the reader place the research problem within the context of their own perspectives about the topic. In addition, concluding your introduction with an explicit roadmap tells the reader that you have a clear understanding of the structural purpose of your paper. In this way, the roadmap acts as a type of promise to yourself and to your readers that you will follow a consistent and coherent approach to addressing the topic of inquiry. Refer to it often to help keep your writing focused and organized.

Cassuto, Leonard. “On the Dissertation: How to Write the Introduction.” The Chronicle of Higher Education , May 28, 2018; Radich, Michael. A Student's Guide to Writing in East Asian Studies . (Cambridge, MA: Harvard University Writing n. d.), pp. 35-37.

• << Previous: Executive Summary
• Next: The C.A.R.S. Model >>
• Last Updated: May 18, 2024 11:38 AM
• URL: https://libguides.usc.edu/writingguide

How to Write a Research Hypothesis

• Research Process
• Peer Review

Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

Make sure your research is error-free before you send it to your preferred journal . Check our our English Editing services to avoid your chances of desk rejection.

Jonny Rhein, BA

See our "Privacy Policy"

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

• A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
• Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
• Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
• Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
• As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

• As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
• Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
• Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.  

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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.

Get science-backed answers as you write with Paperpal's Research feature

How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your research topic
• Capture reader interest
• Summarize existing research
• Position your own approach
• Define your specific research problem and problem statement
• Highlight the novelty and contributions of the study
• Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

• Break through writer’s block. Write your research paper introduction with Paperpal Copilot

Table of Contents

What is the introduction for a research paper, why is the introduction important in a research paper, craft a compelling introduction section with paperpal. try now, 1. introduce the research topic:, 2. determine a research niche:, 3. place your research within the research niche:, craft accurate research paper introductions with paperpal. start writing now, frequently asked questions on research paper introduction, key points to remember.

The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

• Scope: The topic covered in the research paper
• Context: Background of your topic
• Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

• It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
• It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
• It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
• It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

• What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
• What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
• What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

• Highlight the importance of the research field or topic
• Describe the background of the topic
• Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

• Reveal a gap in existing research or oppose an existing assumption
• Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

• State the purpose of your study
• Highlight the key characteristics of your study
• Describe important results
• Highlight the novelty of the study.
• Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

Write a Research Paper Introduction in Minutes with Paperpal

Paperpal Copilot is a generative AI-powered academic writing assistant. It’s trained on millions of published scholarly articles and over 20 years of STM experience. Paperpal Copilot helps authors write better and faster with:

• Real-time writing suggestions
• In-depth checks for language and grammar correction
• Paraphrasing to add variety, ensure academic tone, and trim text to meet journal limits

With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

How to use Paperpal to write the Introduction section

Step 1: Sign up on Paperpal and click on the Copilot feature, under this choose Outlines > Research Article > Introduction

Step 2: Add your unstructured notes or initial draft, whether in English or another language, to Paperpal, which is to be used as the base for your content.

Step 3: Fill in the specifics, such as your field of study, brief description or details you want to include, which will help the AI generate the outline for your Introduction.

Step 4: Use this outline and sentence suggestions to develop your content, adding citations where needed and modifying it to align with your specific research focus.

Step 5: Turn to Paperpal’s granular language checks to refine your content, tailor it to reflect your personal writing style, and ensure it effectively conveys your message.

You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

• Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
• Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
• Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
• Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Scientific Writing Style Guides Explained
• 5 Reasons for Rejection After Peer Review
• Ethical Research Practices For Research with Human Subjects
• 8 Most Effective Ways to Increase Motivation for Thesis Writing 

Practice vs. Practise: Learn the Difference

Academic paraphrasing: why paperpal’s rewrite should be your first choice , you may also like, how to write a high-quality conference paper, academic editing: how to self-edit academic text with..., measuring academic success: definition & strategies for excellence, phd qualifying exam: tips for success , ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., what are journal guidelines on using generative ai..., quillbot review: features, pricing, and free alternatives, what is an academic paper types and elements , should you use ai tools like chatgpt for....

HOW TO: Use Articles for Research: Introduction: Hypothesis/Thesis

• What's a Scholarly Journal?
• Reading the Citation
• Authors' Credentials
• Introduction: Hypothesis/Thesis
• Literature Review
• Research Method
• Results/Data
• Discussion/Conclusions

Hypothesis or Thesis

The first few paragraphs of a journal article serve to introduce the topic, to provide the author's hypothesis or thesis, and to indicate why the research was done.  A thesis or hypothesis is not always clearly labled; you may need to read through the introductory paragraphs to determine what the authors are proposing.

• << Previous: Abstract
• Next: Literature Review >>
• Last Updated: Jan 29, 2024 3:35 PM
• URL: https://libguides.cayuga-cc.edu/1ST-PRIORITY/articles

Module 1: Introduction: What is Research?

Learning Objectives

By the end of this module, you will be able to:

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

Email Updates

How to Get Started on Your First Psychology Experiment

Acquiring even a little expertise in advance makes science research easier..

Updated May 16, 2024 | Reviewed by Ray Parker

• Why Education Is Important
• Find a Child Therapist
• Students often struggle at the beginning of research projects—knowing how to begin.
• Research projects can sometimes be inspired by everyday life or personal concerns.
• Becoming something of an "expert" on a topic in advance makes designing a study go more smoothly.

One of the most rewarding and frustrating parts of my long career as a psychology professor at a small liberal arts college has been guiding students through the senior capstone research experience required near the end of their college years. Each psychology major must conduct an independent experiment in which they collect data to test a hypothesis, analyze the data, write a research paper, and present their results at a college poster session or at a professional conference.

The rewarding part of the process is clear: The students' pride at seeing their poster on display and maybe even getting their name on an article in a professional journal allows us professors to get a glimpse of students being happy and excited—for a change. I also derive great satisfaction from watching a student discover that he or she has an aptitude for research and perhaps start shifting their career plans accordingly.

The frustrating part comes at the beginning of the research process when students are attempting to find a topic to work on. There is a lot of floundering around as students get stuck by doing something that seems to make sense: They begin by trying to “think up a study.”

The problem is that even if the student's research interest is driven by some very personal topic that is deeply relevant to their own life, they simply do not yet know enough to know where to begin. They do not know what has already been done by others, nor do they know how researchers typically attack that topic.

Students also tend to think in terms of mission statements (I want to cure eating disorders) rather than in terms of research questions (Why are people of some ages or genders more susceptible to eating disorders than others?).

Needless to say, attempting to solve a serious, long-standing societal problem in a few weeks while conducting one’s first psychology experiment can be a showstopper.

Even a Little Bit of Expertise Can Go a Long Way

My usual approach to helping students get past this floundering stage is to tell them to try to avoid thinking up a study altogether. Instead, I tell them to conceive of their mission as becoming an “expert” on some topic that they find interesting. They begin by reading journal articles, writing summaries of these articles, and talking to me about them. As the student learns more about the topic, our conversations become more sophisticated and interesting. Researchable questions begin to emerge, and soon, the student is ready to start writing a literature review that will sharpen the focus of their research question.

In short, even a little bit of expertise on a subject makes it infinitely easier to craft an experiment on that topic because the research done by others provides a framework into which the student can fit his or her own work.

This was a lesson I learned early in my career when I was working on my own undergraduate capstone experience. Faced with the necessity of coming up with a research topic and lacking any urgent personal issues that I was trying to resolve, I fell back on what little psychological expertise I had already accumulated.

In a previous psychology course, I had written a literature review on why some information fails to move from short-term memory into long-term memory. The journal articles that I had read for this paper relied primarily on laboratory studies with mice, and the debate that was going on between researchers who had produced different results in their labs revolved around subtle differences in the way that mice were released into the experimental apparatus in the studies.

Because I already had done some homework on this, I had a ready-made research question available: What if the experimental task was set up so that the researcher had no influence on how the mouse entered the apparatus at all? I was able to design a simple animal memory experiment that fit very nicely into the psychological literature that was already out there, and this prevented a lot of angst.

Please note that my undergraduate research project was guided by the “expertise” that I had already acquired rather than by a burning desire to solve some sort of personal or social problem. I guarantee that I had not been walking around as an undergraduate student worrying about why mice forget things, but I was nonetheless able to complete a fun and interesting study.

My first experiment may not have changed the world, but it successfully launched my research career, and I fondly remember it as I work with my students 50 years later.

Frank McAndrew, Ph.D., is the Cornelia H. Dudley Professor of Psychology at Knox College.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

R&D investment and corporate total factor productivity under the heterogeneous environmental regulations: evidence from Chinese micro firms

• Original Paper
• Published: 19 May 2024

Cite this article

• X. Ding   ORCID: orcid.org/0000-0001-7213-1948 1 ,
• Y. Zhang 2   na1 ,
• Y. Fu 2   na1 &

Technological innovation activities are the most effective way to achieve corporate leapfrog development. Based on the Porter effect theory, this paper uses panel data on Chinese manufacturing firms from 2015 to 2018 to construct two-way fixed effects and threshold effects models to explore the impact mechanism of research and development (R&D) investment on corporate total factor productivity (CTFP) under heterogeneous environmental regulations. Baseline regression results indicate that R&D investment significantly promotes CTFP. Meanwhile, we also test the robustness of baseline regression results by replacing the dependent variable, shortening the time windows and adding omitted variables. Moreover, heterogeneity analyses indicate that the contribution of R&D investment to CTFP is more significant in the subgroup regressions of non-SOEs, CEO-dual enterprises and non-heavily polluting enterprises. Economic consequence analysis shows that R&D investment contributes to green innovation performance, financial performance and corporate social responsibility performance by increasing CTFP. Additionally, there is heterogeneity in the moderating effects of market-incentivized environmental regulation (MER), command-and-control environmental regulation (CER) and public participation environmental regulation (PER). MER and PER have moderated mediating effects, but CER does not have a moderated mediating effect. Extended analysis shows that according to the threshold effect test findings, two thresholds exist for MER and one threshold each for PER and CER in the relationship between R&D investment and CTFP. Our findings have important implications in that the government should adopt differentiated environmental regulation policies to support companies in actively carrying out innovation activities, thereby promoting high-quality development.

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

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

The data will be available on request by corresponding author.

Bae J, Oh H, Kim S (2022) Advertising role of CSR and strategic investment in R&D: evidence from the biohealth industry. J Curr Issues Res Advert 44(1):105–121. https://doi.org/10.1080/10641734.2022.2131655

Article   Google Scholar  

Baldi G, Bodmer A (2017) R&D investments and Corporate cash holdings. Econ Innov New Technol. https://doi.org/10.1080/10438599.2017.1378191

Bao Q, Chai H (2022) Environmental regulation, financial resource allocation, and regional green technology innovation efficiency. Discrete Dyn Nat Soc 2022:1–11. https://doi.org/10.1155/2022/7415769

Bengoa M, Román VMS, Pérez P (2017) Do R&D activities matter for productivity? A regional spatial approach assessing the role of human and social capital. Econ Model 60:448–461. https://doi.org/10.1016/j.econmod.2016.09.005

Chen W, Chen S, Wu T (2022a) Research of the impact of heterogeneous environmental regulation on the performance of China’s manufacturing enterprises. Front Environ Sci. https://doi.org/10.3389/fenvs.2022.948611

Chen J, Wang X, Shen W, Tan Y, Matac LM, Samad S (2022b) Environmental Uncertainty, environmental regulation and enterprises’ green technological innovation. Int J Environ Res Public Health 19(16):9781. https://doi.org/10.3390/ijerph19169781

Chen M, Shi V, Wei X (2022c) Environmental regulations, R&D intensity, and enterprise profit rate: understanding firm performance in heavy pollution industries. Front Environ Sci. https://doi.org/10.3389/fenvs.2022.1077209

Dai L, Zhang J, Luo S (2022) Effective R&D capital and total factor productivity: evidence using spatial panel data models. Technol Forecast Soc Chang 183:121886. https://doi.org/10.1016/j.techfore.2022.121886

Dechezleprêtre A, Sato M (2017) The impacts of environmental regulations on competitiveness. Rev Environ Econ Policy 11(2):183–206. https://doi.org/10.1093/reep/rex013

Ding X, Xu Z, Petrovskaya MV, Wu K, Ye L, Sun Y, Makarov VM (2023) Exploring the impact mechanism of executives’ environmental attention on corporate green transformation: evidence from the textual analysis of Chinese companies’ management discussion and analysis. Environ Sci Pollut Res 30(31):76640–76659. https://doi.org/10.1007/s11356-023-27725-4

Edquist H, Henrekson M (2017) Do R&D and ICT affect total factor productivity growth differently? Telecommun Policy 41(2):106–119. https://doi.org/10.1016/j.telpol.2016.11.010

Erdin C, Ozkaya G (2020) R&D investments and quality of life in Turkey. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e04006

Ezzi F, Jarboui A, Zouari-Hadiji R (2020) Exploring the relationship between managerial emotional intelligence, R&D and CSR performance: a mediated moderation analysis. J High Technol Manag Res 31(2):100387. https://doi.org/10.1016/j.hitech.2020.100387

Fan J, Teo T (2022) Will China’s R&D investment improve green innovation performance? an empirical study. Environ Sci Pollut Res 29(26):39331–39344. https://doi.org/10.1007/s11356-021-18464-5

Gao X, Dong Z (2022) Technological innovation and the complexity of imported technology: moderating effects based on environmental regulation. Front Environ Sci. https://doi.org/10.3389/fenvs.2022.996867

Guo X, Li K, Yu S, Wei B (2021) Enterprises’ R&D investment, venture capital syndication and IPO underpricing. Sustainability 13(13):7290. https://doi.org/10.3390/su13137290

Hadlock CJ, Pierce JR (2010) New evidence on measuring financial constraints: moving beyond the KZ index. Rev Financ Stud 23(5):1909–1940. https://doi.org/10.1093/rfs/hhq009

Han C, Zhang WG, Feng ZB (2017) How does environmental regulation remove resource misallocation-an analysis of the first obligatory pollution control in china. China Ind Econ 4:115–134. https://doi.org/10.19581/j.cnki.ciejournal.2017.04.007

Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603. https://doi.org/10.1111/1468-0262.00124

Huang MC, Liou MH, Iwaki Y (2020) The impact of R&D and innovation on global supply Chain transition: GTAP analysis on Japan’s public R&D investment. J Soc Econ Dev 23(S3):447–467. https://doi.org/10.1007/s40847-020-00113-1

Huang H, Qi B, Chen L (2022) Innovation and high-quality development of enterprises—also on the effect of innovation driving the transformation of China’s economic development model. Sustainability 14(14):8440. https://doi.org/10.3390/su14148440

Igartua JJ, Hayes AF (2021) Mediation, moderation, and conditional process analysis: concepts, computations, and some common confusions. Span J Psychol 24:E49. https://doi.org/10.1017/sjp.2021.46

Jiang Y, Qin S, Xu Y (2022) Impact of green credit policy on sustainability performance of high-pollution enterprises. Environ Sci Pollut Res 29:79199–79213. https://doi.org/10.1007/s11356-022-21315-6

Kim SI, Kim KT (2020) The differentiated CSR activities and corporate value. Asian Rev Account 29(1):19–41. https://doi.org/10.1108/ara-05-2020-0067

Lee M, Choi M (2015) Analysis on time-lag effect of research and development investment in the pharmaceutical industry in Korea. Osong Public Health Res Perspect 6(4):241–248. https://doi.org/10.1016/j.phrp.2015.07.001

Levinsohn J, Petrin A (2003) Estimating production functions using inputs to control for unobservables. Rev Econ Stud 70(2):317–341. https://doi.org/10.1111/1467-937x.00246

Li J, Wu X (2022) The S-shaped relationship between R&D investment and green innovation after cross-border merge and acquisition: evidence from China. Environ Sci Pollut Res 29(36):55039–55057. https://doi.org/10.1007/s11356-022-19739-1

Li X, Yang Y (2022) The relationship between air pollution and company risk-taking: the moderating role of Digital Finance. Front Environ Sci 10:988450. https://doi.org/10.3389/fenvs.2022.988450

Li X, Cheng B, Li Y, Duan J, Tian Y (2022) The relationship between enterprise financial risk and R&D investment under the influence of the COVID-19. Front Public Health. https://doi.org/10.3389/fpubh.2022.910758

Liu Z, Ling Y (2022) Structural transformation, total factor productivity and high-quality development. China Econom 17(1):70–82. https://doi.org/10.19602/j.chinaeconomist.2022.01.06

Lu XD, Lian YJ (2012) Estimation of total factor productivity of industrial enterprises in China:1999–2007. China Econ Q 11(02):179–196. https://doi.org/10.13821/j.cnki.ceq.2012.02.013

Lv K, Zhu S, Liang R, Zhao Y (2023) Environmental regulation, breakthrough technological innovation and total factor productivity of firms–evidence from emission charges of China. Appl Econ. https://doi.org/10.1080/00036846.2023.2175776

Mbanyele W, Wang F (2021) Environmental regulation and technological innovation: evidence from China. Environ Sci Pollut Res 29(9):12890–12910. https://doi.org/10.1007/s11356-021-14975-3

Michelino F, Lamberti E, Cammarano A, Caputo M (2015) Open innovation in the pharmaceutical industry: an empirical analysis on context features, internal R&D, and financial performances. IEEE Trans Eng Manage 62(3):421–435. https://doi.org/10.1109/tem.2015.2437076

Mulaessa N, Lin L (2021) How do proactive environmental strategies affect green innovation? The moderating role of environmental regulations and firm performance. Int J Environ Res Public Health 18(17):9083. https://doi.org/10.3390/ijerph18179083

Nandy M (2020) Is there any impact of R&D on financial performance? Evidence from Indian Pharmaceutical Companies. FIIB Bus Rev 9(4):319–334. https://doi.org/10.1177/2319714520981816

Olley GS, Pakes A (1996) The dynamics of productivity in the telecommunications equipment industry. Econometrica 64(6):1263. https://doi.org/10.2307/2171831

Qiu W, Zhang J, Wu H, Irfan M, Ahmad M (2021) The role of innovation investment and institutional quality on green total factor productivity: evidence from 46 countries along the “Belt and Road.” Environ Sci Pollut Res 29(11):16597–16611. https://doi.org/10.1007/s11356-021-16891-y

Salim R, Hassan K, Rahman S (2019) Impact of R&D expenditures, rainfall and temperature variations in agricultural productivity: empirical evidence from Bangladesh. Appl Econ 52(27):2977–2990. https://doi.org/10.1080/00036846.2019.1697422

Shah IH, Kollydas K, Lee PY, Malki I, Chu C (2022) Does R&D investment drive employment growth? Empirical evidence at industry level from japan. Int J Financ Econ. https://doi.org/10.1002/ijfe.2677

Shang Y, Song M, Zhao X (2022) The development of China’s circular economy: from the perspective of environmental regulation. Waste Manag 149:186–198. https://doi.org/10.1016/j.wasman.2022.05.027

Shao L, Liu Q (2022) Decision-making and the contract of the complementary product supply chain considering consumers’ environmental awareness and government green subsidies. Int J Environ Res Public Health 19(5):3100. https://doi.org/10.3390/ijerph19053100

Shi Y, Yang C (2022) How does multidimensional R&D investment affect green innovation? Evidence from China. Front Psychol. https://doi.org/10.3389/fpsyg.2022.947108

Tsamadias C, Pegkas P, Mamatzakis E, Staikouras C (2018) Does R&D, human capital and FDI matter for TFP in OECD countries? Econ Innov New Technol 28(4):386–406. https://doi.org/10.1080/10438599.2018.1502394

Voutsinas I, Tsamadias C (2014) Does research and development capital affect total factor productivity? Evidence from Greece. Econ Innov New Technol 23(7):631–651. https://doi.org/10.1080/10438599.2013.871169

Wang QY (2015) Fixed-effect panel threshold model using Stata. Stata J 15(1):121–13410

Wang J, Zhang Q, Li Q (2020) R&D investment and total factor productivity: an empirical study of the listed companies in the coastal regions of China. J Coastal Res 106(sp1):13. https://doi.org/10.2112/si106-004.1

Wang X, Fan M, Fan Y, Li Y, Tang X (2022a) R&D investment, financing constraints and corporate financial performance: empirical evidence from China. Front Environ Sci. https://doi.org/10.3389/fenvs.2022.1056672

Wang L, Long Y, Li C (2022b) Research on the impact mechanism of heterogeneous environmental regulation on enterprise green technology innovation. J Environ Manag 322:116127. https://doi.org/10.1016/j.jenvman.2022.116127

Woo Y, Kim E, Lim J (2017) The impact of education and R&D investment on regional economic growth. Sustainability 9(5):676. https://doi.org/10.3390/su9050676

Wu YQ, Li J, Ma JC (2022) The effect of innovation investment on environmental total factor productivity. Math Probl Eng 2022:1–13. https://doi.org/10.1155/2022/3100174

Wu J, Segerson K, Wang C (2023) Is environmental regulation the answer to pollution problems in urbanizing economies? J Environ Econ Manag 117:102754. https://doi.org/10.1016/j.jeem.2022.102754

Xiao Z, Peng H, Pan Z (2021) Innovation, external technological environment and the total factor productivity of enterprises. Account Finance 62(1):3–29. https://doi.org/10.1111/acfi.12779

Xu J, Liu F, Shang Y (2020) R&D investment, ESG performance and green innovation performance: evidence from China. Kybernetes 50(3):737–756. https://doi.org/10.1108/k-12-2019-0793

Yan J, Feng L, Denisov A, Steblyanskaya A, Oosterom J-P (2020) Complexity theory for the modern Chinese economy from an information entropy perspective: modeling of economic efficiency and growth potential. PLoS ONE 15(1):e0227206. https://doi.org/10.1371/journal.pone.0227206

Article   CAS   Google Scholar  

Yang B, Zhao Q (2023) The effects of environmental regulation and environmental protection investment on green technology innovation of enterprises in heavily polluting industries—based on threshold and mediation effect models. Front Environ Sci. https://doi.org/10.3389/fenvs.2023.1167581

Ye HY, Jiang S (2020) The impact of technological innovation on industrial green growth under the constraints of heterogeneous environmental regulations. West Forum Econ Manag 31(4):38–46. https://doi.org/10.12181/jjgl.2020.04.05

Yu X (2017) The mechanism of environmental regulation policy and its practice changes-based on the evolution of environmental regulation policy from 1978 to 2016. Forum Sci Technol China 12:15–31. https://doi.org/10.13580/j.cnki.fstc.2017.12.002

Yu F, Shi Y, Wang T (2020) R&D investment and Chinese manufacturing smes’ corporate social responsibility: the moderating role of regional innovative milieu. J Clean Prod 258:120840. https://doi.org/10.1016/j.jclepro.2020.120840

Yu F, Jiang D, Wang T (2022) The impact of green innovation on manufacturing small and medium enterprises corporate social responsibility fulfillment: the moderating role of regional environmental regulation. Corp Soc Responsib Environ Manag 29(3):712–727. https://doi.org/10.1002/csr.2231

Zhang Y, Chen H, He Z (2022) Environmental regulation, R&D investment, and green technology innovation in China: based on the PVAR model. PLoS ONE. https://doi.org/10.1371/journal.pone.0275498

Zhao X, Jia M (2022) Sincerity or hypocrisy: can green M&A achieve corporate environmental governance? Environ Sci Pollut Res 29(18):27339–27351. https://doi.org/10.1007/s11356-021-17464-9

Zhong QL, Liao GM, Lu ZF (2016) Does R&D investment improve the firm productivity? Based on the instrumental variable analysis of talent introduction policy. J Account Econ 30(5):3–16. https://doi.org/10.16314/j.cnki.31-2074/f.2016.05.001

Zhou B, Wu J, Guo S, Hu M, Wang J (2020) Environmental regulation and financial performance of Chinese listed companies. PLoS ONE. https://doi.org/10.1371/journal.pone.0244083

Download references

Acknowledgements

The authors want to thank our editor and reviewers for their valuable comments and advice. The authors also want to acknowledge China Scholarship Council and the contribution of Professor Boris I. Sokolov to this paper.

This research was funded by the China Scholarship Council (Grant Nos. 202008090357, 202210280044, 202210280022, 202008090178).

Author information

Y. Zhang and Y. Fu have contributed to the work equally and should be regarded as co-second authors.

Authors and Affiliations

Faculty of Economics, RUDN University, Miklukho-Maklaya Street, 6, Moscow, Russia, 117198

Institute of Management, Economics and Finance, Kazan Federal University, Butlerova Street, 4, Kazan, Russia, 420010

Y. Zhang & Y. Fu

Institute of Economics and Management, Belgorod State Technological University, Ulitsa Kostyukova, 46, Belgorod, Russia, 308012

You can also search for this author in PubMed   Google Scholar

Contributions

Xiaowei Ding: conceptualization, investigation, methodology, formal analysis, data curation, writing—original draft, and writing—review and editing and corresponding author. Yaqiong Zhang and Yongguang Fu contributed equally to this work: supervision, visualization, validation, and writing—review and editing. Zhenpeng Xu: conceptualization, supervision, and writing—review and editing. All the authors provided critical feedback and helped shape the research, analysis, and manuscript.

Corresponding author

Correspondence to X. Ding .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Editorial responsibility: Maryam Shabani.

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

Ding, X., Zhang, Y., Fu, Y. et al. R&D investment and corporate total factor productivity under the heterogeneous environmental regulations: evidence from Chinese micro firms. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05710-9

Download citation

Received : 09 September 2023

Revised : 05 February 2024

Accepted : 05 May 2024

Published : 19 May 2024

DOI : https://doi.org/10.1007/s13762-024-05710-9

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 regulation constraints
• Research and development investment
• Corporate total factor productivity
• Impact mechanism
• Moderated mediating effect
• Threshold effect

JEL Classification

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

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 20 May 2024

Evolutionary game analysis of data sharing among large and medium-sized enterprises in the perspective of platform empowerment

• Dan Li 1 &
• Xudong Mei 1  

Scientific Reports volume  14 , Article number:  11447 ( 2024 ) Cite this article

Metrics details

• Applied mathematics
• Computational science
• Information technology
• Mathematics and computing

With the swift advancement of the global digital economy, data has emerged as a critical component in fostering the integration of large enterprises with small and medium-sized enterprises (SMEs). Nevertheless, due to disparities in resources and capabilities between these entities, there is a deficiency in the willingness to share data, hindering the full actualization of data’s potential value. Hence, it is imperative to facilitate a novel cooperative development paradigm wherein platforms enable data sharing among large enterprises and SMEs. In this paper, we construct a tripartite evolutionary game model encompassing large enterprises, SMEs, and digital platforms, examine the evolutionary stable strategies adopted by these entities in the data sharing, and use numerical simulation to explore the system’s evolutionary stability under various influencing factors. Contrasting with prior research, this study considers the heterogeneity of enterprise scale and delves into the data sharing dynamics between large enterprises and SMEs. Simultaneously, it positions the digital platform as a player in the game, examining its impact on data sharing among the enterprises. Findings indicate that: (1) SMEs exhibit greater eagerness for data sharing compared to large enterprises, which display a U-shaped influence during the process; (2) Digital platforms are particularly sensitive to costs, with the platform’s initiative and the service quality will affect enterprises strategic choices; (3) Government subsidies positively encourage tripartite cooperation, and robust data security governance framework is crucial for enterprises. Finally, based on the results of the study and combining with the current situation of digital economy development, it puts forward the suggestions for promoting platforms to empower large enterprises and SMEs to realize data sharing and the prospects for future research.

Introduction

In the context of the complex and changing world environment, the global industrial chain supply chain is facing the challenge of insecurity and instability, and the coordination of upstream and downstream support of the industrial chain in various countries has become more difficult. As the main body of the industrial and supply chain, large enterprises and small and medium-sized enterprises (SMEs) play different roles and work together for the stability and development of the industrial and supply chain. Therefore, the synergistic development of large enterprises and SMEs is crucial for enhancing the overall security and adaptability of national industrial and supply chains 1 , 2 . However, the synergistic development of SMEs and large enterprises has not received adequate attention. Both types of enterprises often lack the motivation for cooperative growth and are bereft of efficient mechanisms to foster effective partnerships. In reality, While SMEs are the foundation of the global economy, constituting 90% of all businesses and contributing nearly 70% of global jobs and GDP 3 , disparities in resources and capabilities between them and large enterprises significantly hinder their synergistic development. Internally, it is difficult for SMEs to establish links with large enterprises to obtain resources due to their lack of innovation capital and talents, as well as their weak knowledge and technology base. Although large enterprises have stronger overall strength and objective conditions to assist SMEs in overcoming these deficiencies, concerns over the spillover of their “private property” such as internal core knowledge and technology deter them from offering substantial support. Furthermore, external factors such as market competition, imperfect government policies, and uncertainty risks impede the cooperative progress between large enterprises and SMEs. Traditional cooperative development models and tools are difficult to meet the demand for efficient cooperation among enterprises in the digital era, resulting in the sluggish cooperative development. Therefore, it is of great theoretical and practical significance to investigate how to promote the cooperative development of large enterprises and SMEs in the digital context.

With the progression of the digital revolution, the digital economy has become the “new engine” of economic growth across various countries. The World Bank estimates that the digital economy contributes to more than 15% of GDP, and in the past decade it has been growing at two and a half times faster than physical world GDP 4 . The rapid development of digital economy not only creates new tools (such as digital platform) for the coordinated development of large enterprises and SMEs, but also creates a new model (platform ecological model) for their coordinated development. However, realizing this necessitates leveraging data’s driving role. As a new production factor, data itself can’t generate value, and its value realization significantly differs from traditional resources. Only by collecting, sorting, analyzing and verifying scientifically can we get correct laws and valuable information from data. In this process, it is necessary to dismantle data fragmentation to facilitate comprehensive sharing among different entities, thereby maximizing the value of data through extensive cross-comparison 5 . Many governments have prioritized data sharing as an important development strategy. For example, the European Commission’s Study on Data Sharing between Companies in Europe identifies data sharing among enterprises as a key aspect for advancing Europe’s digital economy. Concurrently, some industry giants, such as Airbus and Walmart, have built their proprietary data sharing platforms, which, by sharing data with SMEs along the industrial and supply chains, collectively enhance production efficiency, product quality, service levels, and the stability of these chains. As an important data infrastructure, platforms have strong resource connection ability and data processing ability 6 . By offering digital services, they can horizontally link large enterprises and SMEs and vertically integrate various departments, providing flexible digital capabilities while improving data sharing efficiency and quality among enterprises, fostering cooperation, reducing information asymmetry 7 , and improves economic benefits 8 . Therefore, studying how to play the role of platform empowerment and promote data sharing among large enterprises and SMEs is not only conducive to their coordinated development and the enhancement of the stability of the industrial and supply chain, but also for releasing the value of data and creating greater benefits for enterprises.

Scholars have extensively explored data sharing among enterprises, primarily focusing on three dimensions: sharing value, influencing factors, and realization methods along with incentive mechanisms. (1) Sharing value. Wang et al. (2022), Aben et al. (2021) and Chen et al. (2021) all found in their studies that data and information sharing among enterprises can reduce information asymmetry and supply chain costs 9 , 10 , 11 . Han et al. 12 constructed an econometric model using Chinese manufacturing firms’ data and demonstrated that data sharing has a significant positive impact on firm productivity. Wang et al. 13 pointed out that data sharing increase the probability and scale of innovation investment of enterprises. Franco et al. 14 found that information sharing is conducive to enhancing the cooperative ability between enterprises, expanding the scale effect, and subsequently improving the Pareto efficiency through a field survey of enterprises. There is a consensus among scholars on the importance of data sharing among enterprises. (2) Influencing factors. Du et al. 15 pointed out that enterprise relationship have a significant effect on the willingness of inter-firm data sharing. Quach et al. 16 proved the impact of data risk on enterprise data sharing. Feng et al. 17 from the perspective of government behavior, found that government incentive policies can influence information sharing among platform enterprises. Guo et al. 18 argued that data sharing may diminish enterprises’ competitiveness , leading to a decline in their willingness to share data. (3) Realization modes and incentives. Lee et al. 19 classified data sharing into three categories: information transmission mode, third-party mode, and information center mode. Zhong et al. 20 comparatively analyzed differences in information sharing modes between manufacturers with or without platform intervention and disparities in information sharing models between retailers. Guan et al. 21 proposed a data sharing incentive strategy based on a two-part compensation contract around the supply chain demand uncertainty issues. Lu et al. 22 compared two incentive strategies—revenue sharing and fixed compensation—and analyzed their impact on provider information sharing through a principal-agent model. Existing researches provide a rich theoretical foundation for enterprise data sharing, but most of them However, most studies generalize both large enterprises and SMEs, overlooking the differences’ impact on data sharing. Therefore, the influencing factors and mechanisms of data sharing between large enterprises and SMEs warrant further exploration.

In the past, enterprises usually use manual entry, script processing, traditional tools to achieve data sharing. However, these methods are inadequate in the digital era, as they fail to meet the needs of modern businesses and present significant drawbacks 23 . Firstly, data sources vary, making it impossible to manage and convert database tables, files, and interfaces uniformly. Secondly, enterprise business systems are independently constructed, leading to the formation of data islands that hinder timely data sharing among enterprises. Thirdly, there is no guarantee mechanism for data security, precluding global monitoring and analysis of data risks. Most scholars believe that information technology (IT) plays a crucial role in addressing these issues. Anandhi 24 highlighted that IT infrastructure serves as the starting point for information transfer, expanding its scope and enabling timely, efficient information sharing. Yu et al. 25 argued that deploying advanced information technology in supply chain systems enhances coordination between upstream and downstream operations, reducing transaction costs among node enterprises. Prajogo et al. 26 noted that network-based information technology facilitates real-time integration and sharing of inventory planning, demand forecasting, and order scheduling information among supply chain enterprises, supporting core companies in balancing supply and demand throughout the supply chain network. It can be seen that IT provides technical support for the improvement of information sharing level. For risks such as leakage and loss in data sharing, the emergence of blockchain technology provides new ideas and solutions for secure data sharing 27 . Yu et al. and Ma et al. 28 , 29 both pointed out that blockchain technology ensures data integrity and resistance to tampering when individual or multiple nodes face attacks through distributed storage, thereby reducing the risk of data leakage. In the digital era, digital platforms as fusion of technology, aggregated data, empowering the application of institutional digital services hubs 30 . They are compatible with various data formats and provide data risk monitoring, constituting an integrated, secure, and efficient data sharing infrastructure encompassing data collection, storage, processing, analysis, and application. Digital platforms contribute significantly to solving efficiency, privacy protection, reliability, cost, and other data sharing issues. However, scholars have not yet fully explored the specific mechanism of how digital platforms can be applied in data sharing between large enterprises and SMEs, which still needs to be further researched.

The essence of the data sharing problem lies in the game of data resources between different subjects, based on the comprehensive consideration of cost and benefit, to find the equilibrium solution that each subject can cooperate well. The traditional game method is based on the assumption of rationality, which has certain limitations. The emergence of evolutionary game theory relaxes the assumption that the participating subjects are all limited rational, which can clearly reflect the dynamic process of the continuous adjustment of their strategies over time. This provides a better research paradigm for analyzing the evolutionary law of economic entities 31 , 32 . Many scholars began to explore the issue of data sharing based on evolutionary game method. Liu et al. 33 built an evolutionary game model of data sharing between logistics platforms and suppliers, identifying factors such as agency fees that impact cooperation. Wei et al. 34 built an evolutionary game model of data sharing among enterprises under government supervision, and used numerical simulation analysis to find that government supervision has little influence on enterprise sharing behavior. Li 35 based on the evolutionary game model, examined the influence of platform information security on information sharing among enterprises. It can be seen that evolutionary game theory has some research in data sharing, which lays a theoretical foundation for this paper, but at present there are almost no scholars to explore how to promote data sharing between large enterprises and SMEs from the perspective of evolutionary game theory.

In summary, enterprise data sharing has become a focal point of both theoretical and practical attention within the context of digitalization. However, current academic research on the factors that influence the willingness of large enterprises and SMEs to share data is insufficient. Furthermore, there is a paucity of discussion on the impact of digital platforms on data sharing between these entities. On the other hand, although some scholars have designed and optimized the data sharing model among enterprises, the analysis of multi-agent game in data sharing is still insufficient. Ignoring the influence of the differences between large enterprises and SMEs on their data sharing willingness. In view of these research gaps, this paper aims to explore the differences in data sharing between large enterprises and SMEs, as well as the role of digital platforms in promoting data sharing among them. And adopt the evolutionary game method to analyze the influencing factors and dynamic evolution path of data sharing among large enterprises and SMEs under the participation of digital platforms, to elucidate the realization mechanism of data sharing among large enterprises and SMEs. It provides theoretical guidance and practical suggestions for establishing efficient data sharing cooperation between large enterprises and SMEs.

Compared with previous literature related to inter-enterprise data sharing, the innovations of this paper are mainly manifested in three aspects: (1) It considers the heterogeneity between large enterprises and SMEs while examining the issue of enterprise data sharing, which breaks through the limitations of previous studies that categorize large enterprises and SMEs as homogeneous enterprises. This expands the research perspective of the enterprise data sharing problem. (2) It investigates the role of digital platforms in data sharing between large enterprises and SMEs, and based on the evolutionary game theory, an evolutionary game model with digital platforms, large enterprises and SMEs as the main body is constructed. This enriches the application of evolutionary game theory within data sharing research. (3) Utilizing numerical simulation, it analyzes the impact and dynamic evolution trajectory of platform service quality, data sharing benefits, costs, subsidies, and risks on the data sharing willingness of large enterprises and SMEs, and further elucidates the factors and mechanisms that influence enterprise data sharing.

This study identifies distinct characteristics in the steady-state data sharing between large enterprises and SMEs. It is found that large enterprises exhibit a “U”-shaped effect in the dynamic evolution of data sharing, whereas SMEs tend to be more proactive. Beyond factors such as revenue, cost, and subsidies, data risk significantly influences the willingness of both enterprise types to share data. For digital platforms, service cost is the primary determinant in enabling data sharing among large enterprises and SMEs. The findings of this paper provide new insights and theoretical foundations for governmental policy formulation or enhancement, holding certain practical implications. For example, it enriches platform governance and other theories related to the platform economy, thereby contributing to its better development. Additionally, the government could formulate incentive policies tailored to the disparities between large enterprises and SMEs to foster data sharing and cooperative growth.

Basic hypothesis and model construction of evolutionary game

Problem description.

In the development of global digital economy, data, as a key production factor, has become an important resource for the development of enterprises in the digital era. Drawing from the Resource-Based View 36 , the value of multiple data sets integrated and reorganized together is much greater than that of a single data set. Consequently, enterprises possessing more comprehensive data sets, with broader scope, can make more precise decisions based on data analytics, thereby mitigating the risk of information asymmetry to a certain extent 37 . This underscores the necessity for data sharing among enterprises 38 . However, in reality, disparities in resources and capabilities between large enterprises and SMEs, coupled with the absence of pertinent mechanisms and the inherent risks associated with data sharing, result in a lack of enthusiasm for data share between these entities.

Considering that many countries around the world have begun to regard data as an important strategic resource, this paper attempts to build a new model of cooperative development in which digital platforms empower large enterprises and SMEs to share data and complement each other’s resources (shown in Fig.  1 ). Such cooperation is predicated on governmental policy incentives and the continuous improvement of the data security law.

Data sharing of large, SMEs empowered by platform.

In the digital era, digital platforms are not only the infrastructure to drive the digital transformation of enterprises, but also a powerful support tool for data sharing between large enterprises and SMEs. Although a few leading enterprises have built “derivative platforms”, such as China Haier’s COSMOPlat, the majority of large enterprises and SMEs are difficult to build mature digital platforms due to deficiencies in relevant technology, talent and other factors. Consequently, they often rely on third-party platforms for empowerment. Therefore, this paper chooses the third-party digital platform as the research object in the subsequent model.

With the empowerment of digital platforms, data sharing between large enterprises and SMEs can break the data silos between enterprises, fostering a more integrated cooperative ecosystem within the supply chain. This not only maximizes the potential value of data but also catalyzes industrial transformation and upgrading, achieving mutual benefits and win–win results. Specifically, large enterprises sharing data resources to SMEs, SMEs can obtain more resources and technical support, thus improving the capacity of digital transformation 39 , and activating the value of data elements empowered by digital platforms to reduce internal costs, increase efficiency, and expand external market growth 40 . Simultaneously, data sharing also promotes the transformation of large enterprises from hierarchical and empirical management to platform-based and data-driven management, thereby elevating quality and efficiency. In addition, platform empowerment is a process of value co-creation between digital and traditional enterprises 41 . The expansive user base of platforms correlates with an increased market scope and enhanced profitability 42 , which in turn bolsters the development of digital industry represented by digital platforms.

Model assumptions

Hypothesis 1.

According to evolutionary game theory 43 , the decision-making process of large enterprises, SMEs and digital platforms is a repeated game in which they constantly adjust and improve their strategic choices according to their own benefits under bounded rationality. The strategy choice of large enterprises and SMEs is (data sharing, no data sharing), and the probability of data sharing of large enterprises is x, the probability of no data sharing is (1 − x), 0 ≤ x ≤ 1; For SMEs, the probability of data sharing is y, and the probability of no data sharing is (1 − y), 0 ≤ y ≤ 1; The strategy choice of digital platform is (cooperation, non-cooperation), the probability of cooperation is z, the probability of non-cooperation is (1 − z), 0 ≤ z ≤ 1.

Hypothesis 2

According to the theory of resource dependence 44 , it is difficult for enterprises to obtain long-term benefits by relying only on their own resources. Sharing data with other businesses can address issues of information asymmetry and adverse selection, reduce communication and coordination costs 45 , and enable enterprises to combine their own characteristics to create unique resources. This approach can provide a competitive edge and enhance organizational efficiency 24 . In this process, enterprises need to conduct in-depth mining and analysis of massive raw data. In the digital era, they can utilize digital platforms that offer data mining, data analytics, machine learning, and other technological services to conduct comprehensive raw data analysis, thereby fully release the value of data 46 . Therefore, it is assumed that \({R}_{l}\) and \({R}_{ms}\) represent the initial returns of large enterprises and SMEs respectively; \({U}_{l}\) and \({U}_{ms}\) respectively represent the maximum benefits that large enterprises and SMEs can obtain from data sharing under the platform empowerment, and \(\alpha\) represent the service quality of digital platforms, which will directly affect the benefits of enterprises in data sharing. Therefore, \(\alpha {U}_{l}\) and \({\alpha U}_{ms}\) respectively represent the direct benefits that large enterprises and SMEs can obtain from data sharing. \({C}_{l}\) and \({C}_{ms}\) respectively represent the costs paid by large enterprises and SMEs to choose digital platform to be empowered. Because large enterprises have more internal organizations and more complicated business data, so they often need to customize services, and their digital inputs are larger than those of SMEs. If only large enterprises are willing to share data and cooperate with digital platform, then large enterprises can obtain platform services and share data with other large enterprises to gain benefits \(\alpha {U}_{l}/2\) , while SMEs will suffer losses \({K}_{ms}\) due to information asymmetry because they do not participate in data sharing; On the contrary, SMEs can gain \(\alpha {U}_{ms}/2\) , while large enterprises will lose \({K}_{l}\) due to information asymmetry. In the case of non-cooperation between digital platforms, enterprises will share data information through traditional methods such as telephone and email. In this case, large enterprises and SMEs need to pay higher human and material costs \({I}_{l}\) and \({I}_{ms}\) respectively to realize data resource sharing and obtain benefits \({V}_{l}\) and \({V}_{ms}\) . If only large enterprises carry out data sharing, due to the reduction of the number of enterprises, the cost to be paid is also reduced to \({I}_{l}/2\) , and the benefits are reduced to \({V}_{l}/2\) ; On the contrary, SMEs need to pay costs \({I}_{ms}/2\) and get benefits \({V}_{ms}/2\) .

Hypothesis 3

According to the Market Failure Model, the market mechanism may sometimes fail to achieve the effective allocation of resources, necessitating government intervention to correct market failures 47 . For SMEs, they often struggle to secure sufficient resources for digital activities through the market mechanism due to constraints in capital, technology, and market access 48 . Although large enterprises have strong comprehensive strength, they are generally reluctant to share data with SMEs because doing so can increase their costs and potentially undermine their competitiveness. As the hub of data sharing, digital platforms may leverage their technological advantages to engage in monopolistic behaviors, which raises the threshold for enterprises to use the platform 49 . This, coupled with information asymmetry between enterprises and platforms with data security problems, which hinders enterprise data sharing. Therefore, government financial subsidies can help SMEs to obtain more resources, enhance the enthusiasm of large enterprises to share data, and incentivize digital platforms to lower barriers, providing secure and reliable data sharing services for both large enterprises and SMEs, thus fostering their cooperative development. It should be noted that the role of government intervention is limited, and its purpose is to help the market adjustment mechanism to return to normal, not to suppress market adjustment, so the government intervention should be moderate 50 . Therefore, it is assumed that in the case where the platform empowers data sharing among large and small enterprises, the government subsidies available to large enterprises, SMEs, and digital platforms are \({S}_{l}\) , \({S}_{ms}\) and \({S}_{p}\) , respectively.

Hypothesis 4

In the digital economy era, data has become an vital asset of enterprises and contain great commercial value. Data sharing implies the flow of data among multiple subjects. However, without comprehensive legal policies and adequate security measures, enterprises may face risks such as data leakage and data misuse. Although digital platforms serve as an ideal infrastructure for data sharing, are also potentially exposed to risks such as insecure transmission, system vulnerabilities, and cyberattacks. For example, Meta (formerly Facebook) experienced three data breaches in 2018, two of which were caused by system vulnerabilities. IBM’s statistics show that more than 25% of data breaches originated from human factors, and again from hacking, with another 48% of breaches resulting from hacking. In response to security issues in data sharing, many countries have begun to formulate relevant laws and policies to strengthen the protection of data security. In addition, the emergence of blockchain and other technologies has provided technical support for digital platforms to reduce data risks and safeguard data security 51 . Therefore, assuming that \(\lambda\) is the degree of perfection of data security governance framework, the degree of perfection of data security guarantee framework will directly affect the security of data. When enterprises do not share data through digital platforms, the possible losses due to data privacy leakage for large enterprises and SMEs are \(\left(1-\lambda \right){D}_{l}\) and \(\left(1-\lambda \right){D}_{ms}\) , respectively. whereas, when enterprises choose to cooperate with digital platforms, the technology of digital platforms can effectively reduce the risk of data sharing, so the possible losses due to data privacy leakage for large enterprises and SMEs are \(\left(1-\lambda \right){D}_{l}/2\) and \(\left(1-\lambda \right){D}_{ms}/\) 2, respectively. Where \({D}_{l}\) and \({D}_{ms}\) are the maximum possible loss of data privacy leakage of large enterprises and SMEs, respectively, and large enterprises tend to have rich data stocks involving more privacy secrets, and the losses arising from data privacy leakage are usually greater than those of SMEs. \(Q\) represents the related cost that the digital platform needs to increase with the continuous improvement of the data security guarantee framework; when \(\lambda \hspace{0.17em}\) < 0.5, \(Q\hspace{0.17em}\) = 0; otherwise, when \(\lambda \hspace{0.17em}\) ≥ 0.5, \(Q\) >0.

Hypothesis 5

According to the theory of Customer Relationship Management (CRM), enterprises can gain sustainable benefits by establishing long-term and intimate customer relationships 52 . In data sharing between large enterprises and SMEs, enterprises act as the platform’s customers, and the digital platform establishes a good cooperative relationship with them by providing high-quality products and services, thereby encouraging enterprise loyalty and reliance on the platform’s offerings. This dependency instills a willingness to perpetuate cooperation with the platform. Consequently, the platform accrues sticky benefits. Therefore, assuming that in the case that the digital platform enables large enterprises and SMEs, \({R}_{p}\) and \({C}_{p}\) respectively represent the platform’s direct revenue and service cost. \(E\) represents the maximum sticky revenue that can be generated by the cooperation between the digital platform and enterprises, and the service quality of the platform will affect the dependence of enterprises on the platform, so \(\alpha E\) represents the sticky revenue that can be obtained by the digital platform.

The meanings of parameters in Hypothesis are shown in Table 1 .

Model construction

According to the above assumptions and different strategy choices of large enterprises, SMEs and digital platforms, the payoff matrix of the three-party mixed strategy game can be constructed (as shown in Table 2 ).

Evolutionary game replication dynamic equation and stability analysis

Replication dynamic equation.

According to the stability theorem of dynamic differential equations, the replication dynamic equations of the players of the three-party game are established respectively. Let the expected revenue of A large enterprise choosing the strategy of “data sharing” and “no data sharing” be A x1 and A x2 respectively, and the average expected revenue is A x3 , then:

The replication dynamic equation is:

Similarly, if the expected revenue of SMEs choosing “data sharing” or “no data sharing” strategy is A y1 and A y2 , and the average expected revenue is A y3 , then:

The replication dynamic equation is as follows:

Similarly, suppose that the expected returns of the digital platform choosing “cooperation” and “non-cooperation” strategies are A z1 and A z2 , and the average expected returns are A z3 , then:

Stability analysis of three-party evolution strategy

The players of the evolutionary game can realize the stability strategy of the system under the joint action. Since mixed strategies are not evolutionarily stable in asymmetric games 53 , only the evolutionary stability of pure strategies is discussed. Set \({\text{F}}\left({\text{x}}\right)={\text{F}}\left({\text{y}}\right)={\text{F}}\left({\text{z}}\right)=0\) . The eight pure strategy equilibria of “large enterprise”, “SMEs” and “digital platform” in the process of game can be obtained E 1 (0,0,0), E 2 (0,0,1), E 3 (0,1,0), E 4 (0,1,1), E 5 (1,0,0), E 6 (1,0,1), E 7 (1,1,0), E 8 (1,1,1). According to the Lyapunov criterion, when all the eigenvalues of the Jacobi matrix are less than 0 30 , the equilibrium point is the evolutionary stable point of the system, and the Jacobi matrix is as follows:

The eigenvalues of the Jacobi matrix corresponding to the above eight pure strategy equilibrium points are shown in Table 3 .

According to the eigenvalues of Jacobi matrix, the equilibrium points E 1 (0,0,0) and E 2 (0,0,1) have eigenvalues of 0, so they are unstable points; The stability of the remaining six equilibrium points can be discussed in the following six situations.

Case 1: when \({V}_{l}-{I}_{l}-\left(1-\lambda \right){D}_{l}\) < \(-{K}_{l}\) , \({V}_{ms}/2-{I}_{ms}/2-\left(1-\lambda \right){D}_{ms}\) > \(0\) , \({ R}_{p}/2+{S}_{p}/2+\alpha E/2-{C}_{p}/2-Q\) <0, only E 3 (0,1,0) is an evolutionarily stable point. In this case, the digital platform chooses not to cooperate because it cannot benefit from enabling. In this case, large enterprises will choose not to share data resources due to the high cost of cooperation with SMEs, but SMEs can still benefit from traditional resource sharing, so SMEs will cooperate with each other.

Case 2: when \(\alpha {U}_{l}+{S}_{l}-{C}_{l}-\left(1-\lambda \right){D}_{l}\) < \(-{K}_{l}\) , \(\alpha {U}_{ms}/2+{S}_{ms}-{C}_{ms}-(1-\lambda ){D}_{ms}\) > \(0\) , \({ R}_{p}/2+{S}_{p}/2+\alpha E/2-{C}_{p}/2-Q\) >0, only E 4 (0,1,1) is the evolutionary equilibrium point. In this case, if large enterprises share data with SMEs through platform empowerment, they will incur great loss, so they will not choose to share data with SMEs. In the case that the platform only enables SMEs, both the platform and SMEs can benefit, and both parties will choose the cooperation strategy.

Case 3: when \({V}_{l}/2-{I}_{l}/2-\left(1-\lambda \right){D}_{l}\) >0, \({V}_{ms}-{I}_{ms}-\left(1-\lambda \right){D}_{ms}\) > \(0\) , \({R}_{p}/2+{S}_{p}/2+\alpha E/2-{C}_{p}/2-Q\) <0, only E 5 (1,0,0) is the evolutionary equilibrium point. In this case, the digital platform chooses not to cooperate because it cannot benefit from enabling. In this case, SMEs will choose not to share data resources due to the high cost of cooperation with large enterprises. However, large enterprises can still benefit from traditional resource sharing, so large enterprises will cooperate with each other.

Case 4: when \(\alpha {U}_{l}/2+{S}_{l}-{C}_{l}-\left(1-\lambda \right){D}_{l}\) > \(0\) , \(\alpha {U}_{ms}+{S}_{ms}-{C}_{ms}-\left(1-\lambda \right){D}_{ms}\) < \(-{K}_{ms}\) , \({ R}_{p}/2+{S}_{p}/2+\alpha E/2-{C}_{p}/2-Q\) >0, only E 6 (1,0,1) is the evolutionary equilibrium point. In this case, if SMEs share data with large enterprises through platform empowerment, there will be a large loss, so they will not choose to share data with large enterprises. In the case that the platform only enables large enterprises, both the platform and large enterprises can benefit, and both parties will choose the cooperation strategy.

Case 5: when \({K}_{l}{+V}_{l}-{I}_{l}-\left(1-\lambda \right){D}_{l}\) >0, \({K}_{ms}+{V}_{ms}-{I}_{ms}-\left(1-\lambda \right){D}_{ms}\) >0, \({ R}_{p}+{S}_{p}+\alpha E-{C}_{p}-Q\) <0, only E 7 (1,1,0) is the evolutionary equilibrium point. In this case, the platform will choose not to participate in the cooperation because it cannot benefit from enabling large enterprises and SMEs to share data. However, large enterprises and SMEs can still benefit from the traditional resource sharing mode, and the two sides will carry out data resource sharing cooperation.

Case 6: when \(\alpha {U}_{l}+{S}_{l}-{C}_{l}-\left(1-\lambda \right){D}_{l}\) > \({K}_{l}\) , \(\alpha {U}_{ms}+{S}_{ms}-{C}_{ms}-\left(1-\lambda \right){D}_{ms}\) > \({K}_{ms}\) , \({R}_{p}+{S}_{p}+\alpha E-{C}_{p}-Q\) >0, only E 8 (1,1,1) is the evolutionary equilibrium point. In this case, when the digital platform enables large enterprises and SMEs to share data, both the platform and the enterprises can benefit, and finally form the ideal state of tripartite cooperation.

Numerical analysis of evolutionary game

To enhance the model simulation results’ proximity to the actual situation, this study augments the interpretability and reliability of the results by parameterizing them with reference to actual cases and pertinent policies. Specifically, we analyze cases from China’s “ Case Collection of Typical Models of Integration and Innovation of Large, Small and Medium-sized Enterprises ” are selected to be analyzed. Among them, Zoomlion Heavy Industries Co., Ltd. leverages a platform to drive cloud and intelligent operations for SMEs within the industrial chain, fostering data integration among large enterprises and SMEs. This integration helps SMEs to achieve 30% increase in scale efficiency, 10% reduction in production cost, and 10% improvement in enterprise cooperative quality, thereby extending the overall value chain of the construction machinery. The COSMOPlat, recognizing the diverse needs of SMEs at various stages of transformation, offers flexible and cost-effective solutions. It also provides customized digital services to large enterprises, propelling the integrated innovation and development of large enterprises and SMEs within the industrial chain. In the process of helping Tianhui Dairy’s transformation, reduce equipment maintenance cost by 40%, improve comprehensive efficiency by 20%, spare parts inventory by 20% 54 .

At present, more and more digital platforms are retaining customers through subscription services, thereby capturing a larger market share and generating sticky revenue. In the “ 2022 Annual Performance Report of Kingdee ” 55 , the annual recurring revenue of Kingdee cloud subscription service accounts for approximately 60% of the total cloud business revenue. Enterprises have different digital investment according to their own strength and scale. According to “ the digital transformation report of Dongguan City ” 56 , the digital investment of enterprises in the next three years is mainly between 1 to 3 million yuan and 3 to 5 million yuan. Regarding government incentive policy, refer to the digital subsidy policy of enterprises in various parts of China, where the subsidy typically ranges from 20 to 30% of the cost. Furthermore, according to the national policy 57 , the subsidy to the platform does not exceed 30% of the cost. Concurrently, in the “ 2022 Data Leakage Cost Report ” released by IBM 58 , it is mentioned that the secondary data leakage rate is close to 50%, causing substantial direct and potential losses to enterprises.

Based on the ideal evolutionary stable point E 8 (1,1,1), combined with the above cases, reports and relevant policies, referring to the division method of large enterprises and SMEs in China 59 , considering the differences in value creation ability 60 , risk bearing ability and other aspects among different subjects, and referring to the idea of parameter assignment by Wei et al. 34 and Li et al. 61 , setting parameters \(\alpha \hspace{0.17em}\) = 0.7, \(\lambda \hspace{0.17em}\) = 0.5, \({R}_{l}\hspace{0.17em}\) = 20, \({R}_{ms}\hspace{0.17em}\) = 10, \({R}_{p}\hspace{0.17em}\) = 4.5, \({U}_{l}\hspace{0.17em}\) = 10, \({U}_{ms}\hspace{0.17em}\) = 6, \({V}_{l}\hspace{0.17em}\) = 5, \({V}_{ms}\hspace{0.17em}\) = 4, \({C}_{l}\hspace{0.17em}\) = 2.5, \({C}_{ms}\hspace{0.17em}\) = 2, \({C}_{p}\hspace{0.17em}\) = 2.5, \({S}_{l}\hspace{0.17em}\) = 0.65, \({S}_{ms}\hspace{0.17em}\) = 0.6, \({S}_{p}\hspace{0.17em}\) = 0.75, \({I}_{l}\hspace{0.17em}\) = 3, \({I}_{ms}\hspace{0.17em}\) = 2.5, \({D}_{l}\hspace{0.17em}\) = 7, \({D}_{ms}\hspace{0.17em}\) = 5, \({K}_{l}=1\) , \({K}_{ms}\) =2, \(E\) =3, \(Q\) =0.5. At the same time, the initial strategy value of the players in the three-party game is set as 0.5, and the evolution path of the players’ strategy selection is numerically analyzed by MATLAB software 62 .

Numerical analysis of three-party evolutionary strategies

Through the simulation analysis of the above values, the results are shown in Fig.  2 . Under the constraints in Case 6, the main body of the tripartite evolutionary game will eventually evolve to the ideal equilibrium state E 8 (1,1,1). In this case, the tripartite strategy is to share data for large enterprises and SMEs, and the digital platform chooses to cooperate with the enterprises.

Numerical analysis of the three-party evolutionary game1.

In addition, Fig.  2 shows that both digital platforms (z) and SMEs (y) demonstrate an increasing trend towards a stable state, with digital platforms evolve to stability in the fastest. For digital platforms, “empowerment” is an important mechanism to promote value co-creation and platform development. Cooperating with enterprises and providing services for them is the primary way for digital platform to gain revenue. Therefore, platforms will increase their willingness to cooperate and have a strong incentive to promote data sharing among enterprises. For SMEs, although data sharing requires higher costs, they can obtain more market information and resource support through data sharing with large enterprises, thus reducing information asymmetry, decreasing production and operation costs, increasing market share and bolstering competitiveness. Therefore, the willingness of SMEs to share data will also gradually increase.

However, it can be found that the evolution path of large enterprises (x) is U-shaped curve, with their data sharing willingness initially declining before ascending. This may be attributed to, on the one hand, large enterprises may incur large losses due to data privacy leakage during data sharing. Therefore, in a short period of time, large enterprises may prefer to protect their own data because of risk considerations, and the motivation for data sharing will decrease. On the other hand, large enterprises face competitive pressures from SMEs. If large enterprises share their data, it may lead to an increase in the competitiveness of SMEs, indirectly imperiling the market position and interests of large enterprises. As a result, large enterprises may adopt conservative strategies and reduce their willingness to share data in the short term.

And as the evolution time goes on, the sharing enthusiasm of digital platforms and SMEs gradually increases. And large enterprises may find after comprehensive deliberation that platform empowerment and data sharing can bring them more benefits, such as accessing new business opportunities brought by external data, enhancing efficiency, and reducing costs. As a result, the probability of large enterprises’ data sharing gradually rises. As the game deepens, all parties gradually see the possibility of win–win cooperation, thereby augmenting the willingness to cooperate. Ultimately, the ideal situation of digital platform empowering data sharing among large enterprises and SMEs is realized.

Influence of the change of the initial strategy value of the platform on the strategy selection of enterprises

In order to analyze the influence of the change of the initial strategy value of the digital platform on the strategy choice of the enterprise, the values of z are set as 0.1, 0.2, 0.4, 0.6 and 0.8 respectively, and the other parameters remain unchanged. The evolution results are shown in Fig.  3 a and b.

( a ) The influence of z change on the strategy choice of large enterprises ( b ) the influence of z change on the strategy choice of SMEs2.

According to Fig.  3 a and b, when z ≥ 0.2, both x and y converge to 1, with y converging faster than x. Conversely, when z ≤ 0.1, both x and y converge to 0. This suggests that there is a specific threshold above which the probability of cooperation with digital platforms becomes sufficiently high to induce enterprises to opt for data sharing. This reflects that at lower probabilities of cooperation, enterprises consider it challenging to maximize the value of data through conventional sharing methods. Coupled with inadequate security technology, the risk of data privacy breaches is elevated, potentially leading to more detriment than benefit for the enterprise. Consequently, when enthusiasm for digital platform cooperation wanes, risk aversion dictates that enterprises abstain from data sharing. And large enterprises will choose not to share data more quickly due to the greater potential loss.

In addition, Fig.  3 a indicates that as a platform’s willingness to cooperate increases, the U-shaped effect observed in the evolution of large enterprises’ sharing strategies diminishes. This could be attributed to the digital platforms can reduce the difficulty and risk of data sharing by providing technical support and services to better create value for enterprises. Therefore, large enterprises will increase the data sharing motivation, thus reducing the U-shaped effect. Meanwhile, from Fig.  3 b, it can be found that the willingness to cooperate with digital platforms correlates positively with SMEs’ data sharing motivation, which exceeds that of large enterprises. Given SMEs’ robust demand for data sharing, an augmented cooperative disposition from the platform is likely to draw more SMEs to participate. Therefore, influenced positively by the empowerment of SMEs via the platform, large enterprises may enhance their data sharing inclination, thus reducing the U-shaped effect.

The influence of subsidies and costs on the selection of evolutionary game strategies

The influence of data sharing cost on the strategy choice of the firm when the subsidy remains unchanged

Under the condition that the government subsidy remains unchanged \({C}_{l}\) , in order to analyze the influence of the change of the input cost of data sharing through platform empowerment on the strategy choice of large enterprises and SMEs, we set \({C}_{l}\) as 1.5, 2, 2.5, 3 and 3.5 respectively; Set \({C}_{ms}\) be 1, 1.5, 2, 2.5, 3, and the remaining parameter values remain unchanged. The evolution results are shown in Fig.  4 a and b.

Observations from Fig.  4 a and b indicate that when \({C}_{l}\hspace{0.17em}\) ≤ 3 and \({C}_{ms}\hspace{0.17em}\) ≤ 2.5, both x and y converge to 1, and the convergence speed of y is faster than that of x. Conversely, when \({C}_{l}\)  ≥ 3.5 and \({C}_{ms}\)  ≥ 3, both x and y converge to 0. data sharing becomes a preferred option for large enterprises and SMEs when the associated costs are below a certain threshold. This reflects that low data sharing costs enable both enterprise types to derive benefits sufficient enough to offset their costs. At the same time, the cooperative relationship helps both parties to better recognize the long-term value and potential benefits of data sharing. Therefore, as the cost of data sharing decreases, the more motivated SMEs and large enterprises will engage in data sharing. However, when the cost for data sharing is high, large enterprises and SMEs will reassess the benefits and costs of data sharing, potentially leading to a decision against it if the costs outweigh the benefits.

In addition, comparing Fig.  4 a and b reveals that SMEs will choose not to data share more quickly than large enterprises when the cost is too high. This may be due to the fact that SMEs usually have relatively low risk tolerance. When faced with costly data sharing, they may be more inclined to adopt a conservative strategy to avoid taking excessive risks. As a result, SMEs will be quicker to opt out of data sharing.

The impact of subsidies and data sharing costs on the strategy choice of enterprises

If the government subsidy is adjusted with the change of enterprise data sharing cost, in the case of (1) \({C}_{l}\) and \({C}_{ms}\) change, according to the government subsidy range is 20–30% of enterprise digital cost, let \({S}_{l}\) be 0.4, 0.5, 0.65, 0.8, 0.9 respectively; Let \({S}_{ms}\) be 0.3, 0.45, 0.6, 0.75 and 0.9 respectively, and the remaining parameters remain unchanged. The evolution results are shown in Fig.  4 c and d.

It can be seen from Fig.  4 c and d that both x and y converge to 1. This reflects the efficacy of a flexible subsidy policy as a positive incentive, which can effectively compensate for the cost of data sharing among different enterprises and boost them participate in data sharing. In addition, government subsidy is also a signaling effect. It conveys to the market the government’s attitude of encouraging and supporting data sharing, which helps to enhance the willingness to cooperate and trust among enterprises. Therefore, a flexible subsidy policy can be adjusted according to the specific situation of different enterprises, thereby facilitating the realization of data sharing between large enterprises and SMEs.

The impact of digital platform service cost on the choice of platform strategy when the subsidy remains unchanged

When the government subsidy remains unchanged, in order to analyze the influence of the change of digital platform service cost on the strategy choice of the game players, set \({C}_{p}\) as 2, 4, 6, 8 and 10 respectively, and the remaining parameters remain unchanged. The evolution results are shown in Fig.  5 a.

It can be seen from Fig.  5 a that when \({C}_{p}\)  ≤ 6, z converges to 1, and the \({C}_{p}\) smaller it is, the faster the convergence speed is. When \({C}_{p}\)  ≥ 8, z decreases continuously as it increases. Therefore, there is a threshold at which digital platforms will choose not to cooperate with large enterprises and SMEs when the cost of the services greater than the threshold. This reflects that while empowering enterprises is an important way for platforms to gain revenue, when the cost of services exceed the gains may lead them to deem such partnerships economically impractical. As a result, digital platforms may focus their resources on more profitable areas and avoid high-cost data-sharing service cooperations with large enterprises and SMEs.

The impact of subsidies and digital platform service costs on platform strategy selection

If the government subsidy is adjusted with the change of enterprise data sharing cost, in \({C}_{p}\) the case of change in (3), according to the government subsidy is 30% of the digital platform cost \({S}_{p}\) , let them be 0.6, 1.2, 1.8, 2.4 and 3 respectively, and the remaining parameter values remain unchanged. The evolution results are shown in Fig.  5 b.

It can be seen from Fig.  5 b that the government can alleviate the pressure on the operating costs of platforms by enhancing the subsidies, thereby stimulating platforms to empower enterprises. However, when the costs that the platform needs to pay is too high, but government subsidies are limited. If the revenue generated from digital platform-enterprise cooperations fails to offset their costs, platforms may still be disinclined to engage in cooperation. At this time, although government subsidies can improve the enthusiasm of platform cooperation to a certain extent, they cannot achieve a stable state of cooperation.

The impact of government subsidies on the selection of evolutionary game strategies when the cost is constant

( a ) Influence of \({C}_{l}\) change on the strategy choice of large enterprises ( b ) Influence of \({C}_{ms}\) change on the strategy choice of SMEs c Influence of \({C}_{l}\) and \({S}_{l}\) change on the strategy choice of large enterprises d Influence of \({C}_{ms}\) and \({S}_{ms}\) change on the strategy choice of SMEs.

( a ) Influence of \({C}_{p}\) change on the strategy choice of platform ( b ) Influence of \({C}_{p}\) and \({S}_{p}\) change on the strategy choice of platform.

Under the condition that the cost of the enterprise and the platform remain unchanged, in order to analyze the influence of the change of the government subsidy intensity on the strategy choice of the game subject, set \({S}_{l}\) are 0.125, 0.25, 0.5, 0.75 and 1, and the values of \({S}_{ms}\) are 0.1, 0.3, 0.5, 0.7 and 0.9, respectively. \({S}_{p}\) are 0.125, 0.25, 0.5, 0.75 and 1, respectively, and the remaining parameters remain unchanged. The evolution results are shown in Fig.  6 .

Influence of government subsidies on the strategy selection of the tripartite evolutionary game.

It can be seen from Fig.  6 that when \({S}_{l}\)  ≥ 0.25, \({S}_{ms}\)  ≥ 0.3 and \({S}_{p}\)  ≥ 0.25, x, y and z all converge to 1, and the \({S}_{l}\) , \({S}_{ms}\) and \({S}_{p}\) greater, the faster the convergence speed is; When \({S}_{l}\)  ≤ 0.125, ≤ 0.1 and \({S}_{ms}\)  ≤ 0.125, x and y all converge to 0, and z still converges to near 1. This may be because the government subsidies not sufficient to cover the risks and costs associated with enterprise data sharing.

Large enterprises and SMEs need to pay a certain cost for data sharing, and there are potential risks of data privacy leakage and data misuse. Consequently, they prefer not to share data when government subsidies are not sufficient to cover these costs and risks. Despite this, cooperation with enterprises is an important way for platforms to gain revenue. Platforms, perhaps with a more strategic foresight, are willing to invest resources to foster enterprise data sharing, as it can augment their user base and data flow, hereby positively impacting their long-term growth. Therefore, even with low government subsidies, platforms are still highly willing to cooperate.

The impact of digital platform service quality on enterprise strategy choice

Since the quality of service provided by the digital platform directly impacts the revenue generated from enterprise data sharing, this paper analyzes the influence of varying service quality on the strategic choices of game players. The values of \(\alpha\) are set to 0.2, 0.3, 0.5, 0.8 and 0.9 respectively, and the remaining parameter values remain unchanged. The evolution results are shown in Fig.  7 a, b.

( a ) Influence of \(\alpha\) change on strategy selection of large enterprises ( b ) Influence of \(\alpha\) change on strategy selection of SMES.

According to Figs.  2 , 7 a and b, when \(\alpha\)  ≥ 0.7, both x and y converge to 1; When \(\alpha\)  ≤ 0.5, both x and y converge to 0, with x converging faster than y. Therefore, there exists a certain threshold. When platform’s service quality surpasses this threshold, large enterprises and SMEs will cooperate with platform to share data. This reflects that an increase in the quality of the platform’s services can better meet the needs and expectations of large enterprises and SMEs. If the platform can provide high-quality data processing and analysis services, large enterprises and SMEs are more likely to believe that platform has the ability to utilize the shared data to create greater value for them. Simultaneously, the higher quality of digital platform services, the more sticky benefits platform can gain from cooperation, thereby increasing platform’s incentive to cooperate, as well as the willingness of large enterprises and SMEs to share data.

In addition, it can be found that higher platform service quality has a significant effect on reducing the U-shaped effect in the evolution of large enterprises. This is due to the fact that when platform offers high-quality services, it not only reduces the cost and risk of data sharing for large enterprises, but also facilitates the establishment of long-term cooperative relationships between large enterprises and other SMEs, as well as continuous data sharing. Consequently, it enhances the revenue of large enterprises, thus reducing the U-shaped effect.

Influence of the improvement degree of data security guarantee framework on the selection of evolutionary game strategies

In order to analyze the influence of changes in the improvement degree of data security assurance framework on the strategy selection of each game player, set the values of \(\lambda\) are 0.2, 0.3, 0.4, 0.6, 0.8 respectively, and when \(\lambda\)  < 0.5, \(Q\)  = 0; When \(\lambda\)  = 0.6 and \(\lambda\)  = 0.8, the corresponding values of \(Q\) are 1 and 2 respectively, and the remaining parameter values remain unchanged. The evolution results are shown in Fig.  8 a–c.

( a ) Influence of \(\lambda\) change on the strategy choice of large enterprises ( b ) Influence of \(\lambda\) change on the strategy choice of SMEs c Influence of \(\lambda\) and \(Q\) change on the strategy choice of platform.

It can be seen from Fig.  2 , 8 a–c that when \(\lambda\)  ≥ 0.5, x, y and z all converge to 1, with an increased value of \(\lambda\) correlating to a faster convergence rate. Conversely, for \(\lambda\)  ≤ 0.4, both x and y converge to 0, while z gradually evolves to around 0.8 as \(\lambda\) decreases. Therefore, there is a certain threshold, and when the degree of perfection of the data security guarantee framework exceeds this threshold, enterprises will positively engage in data sharing, and digital platforms will choose to cooperate with them.

This phenomenon reflects that when the government can provide reliable data security, it can reduce the risk of data privacy leakage and thus promote data sharing among enterprises. At the same time, the government’s legal policies will strengthen the regulation of digital platforms, prompting them to strengthen their data security capabilities and reduce inherent data risk, so enterprises will trust platforms more and be willing to share data through cooperation with them. Although the technical cost of platforms increased due to the continuous improvement for data security, platforms can still benefit from cooperation with enterprises in the long-term, so them will choose to cooperate with enterprises. Below this threshold, however, the heightened risk of data privacy leakage deters data sharing, especially among large enterprises that, relative to SMEs, hasten to withhold data due to substantial potential losses.

In addition, the refinement of the data security guarantee framework has a positive effect on reducing the U-shaped effect of large enterprises. This is because a comprehensive data security guarantee framework can help large enterprises greatly reduce the security risks associated with data sharing, protect their interests and thus dispelling their apprehensions regarding data risks, effectively alleviating their U-shaped effect in data sharing.

However, when the values of \(\lambda\) are respectively 0.5, 0.6 and 0.8, and the corresponding values of \(Q\) are respectively 1.5, 3 and 6, the remaining parameter values remain unchanged, and the evolution results are shown in Fig.  9 . When \(\lambda\)  = 0.8 and \(Q\)  = 6, although x and y converge to 1, z converges to 0. This is because, as the data security guarantee framework continues to improve, digital platforms will choose not to cooperate when the costs they have to pay exceed what they can afford. However, at this time, as the risk of data leakage is greatly reduced, data sharing through traditional methods can still be beneficial, so large enterprises and SMEs will choose to share data and cooperate.

Influence of \(\lambda\) and \(Q\) changes on the strategy selection of the players in the game.

Influence of loss caused by information asymmetry on strategy choice of enterprises

In order to analyze the influence of the loss caused by information asymmetry on the strategy choice of the game players, let the values of \({K}_{l}\) are 0.25, 0.5, 1, 2 and 3 respectively; \({K}_{ms}\) are 0.5, 1, 2, 3, 4, and the remaining parameters remain unchanged. The evolution results are shown in Fig.  10 a, b.

( a ) Influence of \({K}_{l}\) change on the strategic choice of large enterprises ( b ) Influence of \({K}_{ms}\) change on the strategic choice of SMEs.

As observed in Fig.  10 a, b, for values of \({K}_{l}\)  ≥ 1 and \({K}_{ms}\)  ≥ 2, both x and y converge to 1, and the greater, the \({K}_{l}\) and \({K}_{ms}\) faster the convergence speed. Conversely, when \({K}_{l}\hspace{0.17em}\) ≤ 0.5 and \({K}_{ms}\hspace{0.17em}\) ≤ 1, both x and y converge to 0. Therefore, for large enterprises and SMEs respectively, there exists a threshold below which large enterprises and SMEs against data sharing when their losses from information asymmetry are less than this threshold. This reflects the fact that large enterprises and SMEs may choose not to share data due to higher cost and data risk considerations when their losses from information asymmetry are relatively low, as such sharing does not mitigate losses but rather augments costs. However, once the loss attributable to information asymmetry exceeds this threshold, there is a heightened incentive for data sharing among enterprises, with SMEs displaying a marginally higher motivation compared to large enterprises.

This phenomenon reflects that when large enterprises and SMEs incur larger losses due to information asymmetry, they may realize that has an increasing impact on their benefits. In this case, in order to minimize losses and increase competitiveness, large enterprises and SMEs may more positively choose data sharing strategies as a way to obtain more information. Additionally, considering the disparities between SMEs and large enterprises in terms of capabilities and resource availability, SMEs may be more reliant on external resources and exhibit a lower tolerance for the risks associated with information asymmetry. Therefore, SMEs are more willing to engage in data sharing than large enterprises.

This study employs evolutionary game theory to investigate the strategic interactions among large enterprises, SMEs and digital platforms. It analyzes the factors influencing the system’s evolution towards an ideal state under varying parameter conditions, leading to the following conclusions.

Due to the large risk loss of data sharing, a U-shaped effect is observed in large enterprises, leading to a temporary decline in their enthusiasm for data sharing. The enhancement of platform service quality and the continuous improvement of data security guarantee framework, significantly mitigates the U-shaped effect.

Government subsidies have a positive incentive and moderating effect on data sharing for platform-enabled enterprises. When enterprises or platforms face significant cost pressures, augmenting government subsidies can facilitate tripartite cooperation and realize data sharing. Compared with platforms, enterprises are more sensitive to changes in subsidies, while platform strategy choices are more influenced by costs.

Data security guarantee framework is a critical factor affecting the realizing of data sharing. When the framework reaches a certain level, enterprises will still seek opportunities to share data, even in the absence of platforms cooperation. Additionally, the improvement of platforms internal security and service quality, and the reduction of costs of enterprises, can promote enterprises to share data through platforms. Furthermore, the data sharing enthusiasm of SMEs is higher than large enterprises. The loss caused by information asymmetry will also affect the decision of enterprises.

In this study, under the perspective of platform empowerment, by constructing an evolutionary game model between large enterprises, SMEs and digital platforms, analyzing the influence of multiple factors—such as cost and data quality—on the strategic choices of each game subject, and simulating the equilibrium point of the system’s evolutionary stabilization strategy under the simulation of different influencing factors with the use of MATLAB software. This study investigates the evolutionary path of digital platforms empowering large enterprises and SMEs to share data and the realization mechanism.

The findings of this paper confirm some of the previous literature, for example, data risk is a key factor affecting the willingness of enterprises to share data 63 , 64 , and the application of digital security technology is conducive to reducing the risk of data privacy leakage, which is conducive to the promotion of data sharing 65 . Additionally, the study indicates that digital platforms can positively impact on enterprise data sharing, and this finding is also generally consistent with the viewpoints mentioned in the previous outlook on future enterprise data sharing 66 .

Moreover, this paper presents some original and interesting findings. Firstly, there is a “U-shaped” effect where the willingness to share data initially decreases and then increases among large enterprises, potentially due to the greater data risks associated with data sharing within this enterprise size. Secondly, SMEs are more positive than large enterprises in data sharing, probably because SMEs usually have more limited resources and capabilities; sharing data with large enterprises can gain more business resources and market share, thus enhancing their competitiveness and realizing better development. Finally, large enterprises and SMEs may engage in data sharing even without the empowerment of digital platforms, but only if the government improves the data security framework to create a secure and reliable environment for inter-enterprise data sharing.

Based on the above results and findings, and in conjunction with the current state of global digital economy progress, the following countermeasures are proposed.

Strengthen the role of government support and guidance, and enhance the level of regularized supervision. Governments can flexibly formulate subsidy incentive policies based on the different characteristics and roles of enterprises and platforms; strengthen guidance for the cooperative development of large enterprises and SMEs, promote the establishment of a data sharing mechanism, and push forward the in-depth integration of the whole industrial chain. Concurrently, it should accelerate the improvement of the data sharing and protection management frameworks, cultivate and grow the security industry, guarantee data safety and promote the sustainable development of the digital economy.

Enterprises should raise awareness of cooperative digital transformation and activate the value of data elements. Large enterprises and SMEs should capitalize on their unique strengths, integrate and innovate development, and jointly improve the modernization level of the industrial chain; heighten their awareness of data as an asset and its security, and refine the data security management hierarchy. Simultaneously, they should strengthen cooperation with digital platforms, enhancing their capacity to integrate and leverage both internal and external data, release the potential value of data, and reduce the asymmetry of information through the complementary sharing of data resources to achieve cost decreasing and benefit increasing.

Give full play to the data-driven empowerment function of digital platforms and grow the scale of platform economy. Platforms should augment their technological innovation prowess, tackle core data security technologies, and ensure the safety of data circulation. They also should intensify cooperation and knowledge exchange among platforms and between platforms and enterprises, creating an interconnected platform ecosystem. Furthermore, platforms should develop transformative products and services tailored to enterprise needs, continually refining data analysis and application skills to attract businesses to join and utilize the platform, thus expanding its market scope and value.

As entering the digital economy, data has become the key link driving the integration between large enterprises and SMEs. It is imperative to study how to promote data sharing for value creation among large enterprises and SMEs. By constructing an evolutionary game model to explore the dynamic evolution process and steady state of platform-enabled data sharing among large enterprises and SMEs under the interaction of different influencing factors, so as to discover the mechanism of data sharing among large enterprises and SMEs. This study finds that cost is the key factor affecting the stability of tripartite cooperation, in addition to improving the service quality of platforms, increasing government subsidies, and strengthening data security protection are all conducive to the promotion of data sharing among large enterprises and SMEs. In the case of data sharing, SMEs and platforms are more motivated than large enterprises, while large enterprises have a U-shaped dynamic change of initial decline followed by an increase motivation in the process.

The paper makes three main theoretical contributions. Firstly, it introduces evolutionary game theory into the research on data sharing, considering the dynamics and decision-making games of digital platforms, large enterprises, and SMEs. This provides a new analytical framework and theoretical perspective for analyzing enterprises data sharing. Secondly, it explores the application of evolutionary game theory in the context of the digital economy, expanding its potential fields of application. Finally, by analyzing the role of digital platforms in promoting data sharing among large enterprises and SMEs, it reveals the internal mechanism of platform empowerment and supports improvements in platform economy and related theories.

The pragmatic significance of this paper is mainly to promote the construction of data sharing and co-development ecosystem between large enterprises and SMEs. On the one hand, it can provide suggestions for the government to formulate data sharing policies and enterprises to choose data sharing strategies. On the other hand, it helps the platform to optimize its service model and fully release the empowerment effect. So as to enhance the attractiveness and user stickiness, and promote the platform’s economic development.

However, this paper also has certain research limitations. Firstly, this paper does not consider the possibility of opportunistic behavior among enterprises, wherein some may exploit data shared by others but not share their own data. If there is no effective punishment mechanism or it is not strong enough, such behavior could proliferate, discouraging data sharing and ultimately undermining the cooperative mechanism. Secondly, although this paper sets numerical simulation parameters by referring to real cases to improve the results reliability, the referenced cases may not represent all situations in reality. Since there may be significant differences in data sharing among enterprises in different industries, the research results may still have some deviations. In addition, the study has not fully considered the long-term impact of changes in the external environment on data sharing, which may change over time, thus affecting the strategic choices and evolutionary paths of data sharing between large enterprises and SMEs.

Against the above research limitations, future research can delve deeper and expand from multiple perspectives. Firstly, future research can consider the impact of opportunistic behavior on data sharing between large enterprises and SMEs. It is important to study how opportunistic behavior affects the willingness of enterprises to share data, and design reasonable reward and punishment mechanisms to circumvent it as much as possible, so as to promote data sharing among enterprises. Secondly, future research can collect a wider range of data and construct a more comprehensive model by analyzing multiple typical cases to improve the authenticity and generalizability of the research results. And we can construct econometric models for empirical analysis by collecting data from different industries to explore the differences in data sharing among different industries, then further refine the mechanisms of data sharing among large enterprises and SMEs empowered by digital platforms. Moreover, future research can focus on how the external environment affects the dynamic evolution of data sharing, such as the changes in policies, so as to propose more targeted and adaptive strategies. Through these efforts, future research will be able to further enrich and develop the research’s content, offering deeper and comprehensive theoretical support and practical suggestions for data sharing among large enterprises and SMEs.

Data availability

All data generated or analysed during this study are included in this published article [and all data are available from the corresponding author upon reasonable request].

Jang, H., Lee, K. & Yoon, B. Development of an open innovation model for R&D collaboration between large firms and small-medium enterprises (SMES) in manufacturing industries. Int. J. Innov. Manag. 21 (01), 1750002 (2017).

Article   Google Scholar  

Brink, T. SME routes for innovation collaboration with larger enterprises. Ind. Mark. Manage. 64 , 122–134 (2017).

International Labour Organization (ILO), The power of small: Unlocking the potential of SMEs, 2019, https://www.ilo.org/infostories/en-GB/Stories/Employment/SMEs#power-of-small . (accessed 19 December 2023)

World Economic Forum, Digital trust: How to unleash the trillion-dollar opportunity for our global economy, 2022, https://www.weforum.org/agenda/2022/08/digital-trust-how-to-unleash-the-trillion-dollar-opportunity-for-our-global-economy/#:~:text=The%20World%20Bank%20estimates%20that,faster%20than%20physical%20world%20GDP . (accessed 19 December 2023)

Liang, F. et al. A survey on big data market: Pricing, trading and protection. Ieee Access 6 , 15132–15154 (2018).

China Academy of Information and Communications Technology, White Paper on Data Infrastructure 2019, 2019, http://www.caict.ac.cn/kxyj/qwfb/bps/201911/P020191118645668782762.pdf (accessed 19 December 2023)

Wu, F., Hu, H., Lin, H. & Ren, X. Digital transformation of enterprises and capital market performance-empirical evidence from stock liquidity. Manage. World 37 (7), 130–144 (2021).

Google Scholar  

Asadi, S., Nilashi, M., Iranmanesh, M., Hyun, S. S. & Rezvani, A. Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach. Technovation 118 , 102426 (2022).

Wang, L. & Wang, Y. Supply chain financial service management system based on block chain IoT data sharing and edge computing. Alex. Eng. J. 61 (1), 147–158 (2022).

Aben, T. A. E., Van Der Valk, W., Roehrich, J. K. & Selviaridis, K. Managing information asymmetry in public–private relationships undergoing a digital transformation: The role of contractual and relational governance. Int. J. Oper. Prod. Manag. 41 , 1145–1191. https://doi.org/10.1108/ijopm-09-2020-0675 (2021).

Chen, Z. & Huang, L. Digital twins for information-sharing in remanufacturing supply chain: A review. Energy 220 , 119712 (2021).

Han, M., Zhang, S. & Gu, X. Can information sharing improve enterprise productivity? -Evidence from the survey data of manufacturing enterprises in China. Res. Indus. Econ. 01 , 42–56. https://doi.org/10.13269/j.cnki.ier.2020.01.004 (2020).

Wang, Z. & Wei, L. Is information sharing beneficial to collaborative innovation of manufacturing enterprises?-An empirical analysis based on the survey data of business environment of enterprises in China. Contemp. Finance Econ. 10 , 95–106. https://doi.org/10.13676/j.cnki.cn36-1030/f.2019 (2019).

Franco, M. & Haase, H. The role of reputation in the business cooperation process: Multiple case studies in small and medium-sized enterprises. J. Strategy Manag. 14 (1), 82–95 (2021).

Du, T. C., Lai, V. S., Cheung, W. & Cui, X. Willingness to share information in a supply chain: A partnership-data-process perspective. Inf. Manag. 49 (2), 89–98 (2012).

Quach, S., Thaichon, P., Martin, K. D., Weaven, S. & Palmatier, R. W. Digital technologies: tensions in privacy and data. Journal of the Academy of Marketing Science 50 , 1299–1323. https://doi.org/10.1007/s11747-022-00845-y (2022).

Article   PubMed   PubMed Central   Google Scholar  

Feng, N., Ren, B., Huang, Z. & Li, M. Research on information sharing incentive mechanism of industrial internet platform in digital economy. J. Beijing Jiaotong Univ. (Soc. Sci. Edition) 21 (2), 1–13 (2021).

CAS   Google Scholar  

Guo, B., Deng, X., Guan, Q., Tian, J. & Zheng, X. An incentive mechanism for cross-organization data sharing based on data competitiveness. IEEE Access 6 , 72836–72844 (2018).

Lee, H. L. & Whang, S. Information sharing in a supply chain. Int. J. Manuf. Technol. Manag. 1 (1), 79–93 (2000).

Zhong, Q., Wang, J., Zou, Z. & Lai, X. The incentives for information sharing in online retail platforms. Transp. Res. Part E Logist. Transp. Rev. 172 , 103050 (2023).

Guan, Z., Zhang, X. & Dan, B. Information sharing and incentive of supply chain in which manufacturer’s service input affects sales under uncertain demand. China Manag. Sci. 27 (10), 56–65. https://doi.org/10.16381/j.CNKI.ISSN1003-207x,2019.10 (2019).

Lu, A. & Liu, J. Research on information sharing incentive strategy of logistics service supply chain. Res. Sci. Technol. Manag. 07 , 221–225 (2019).

Han, G. & Dong, M. Sustainable regulation of information sharing with electronic data interchange by a trust-embedded contract. Sustainability 9 (6), 964 (2017).

Bharadwaj, A. S. A resource-based perspective on information technology capability and firm performance An empirical investigation. MIS Q. 24 (1), 169–196 (2000).

Yu, Y., Huo, B. & Zhang, Z. J. Impact of information technology on supply chain integration and company performance: Evidence from cross-border e-commerce companies in China. J. Enterp. Inf. Manag. 34 (1), 460–489 (2021).

Prajogo, D. & Olhager, J. Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. Int. J. Prod. Econ. 135 (1), 514–522 (2012).

Wen, Q., Gao, Y., Chen, Z., & Wu, D. (2019, May). A blockchain-based data sharing scheme in the supply chain by IIoT. In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (pp. 695–700). IEEE.

Yu, K., Tan, L., Aloqaily, M., Yang, H. & Jararweh, Y. Blockchain-enhanced data sharing with traceable and direct revocation in IIoT. IEEE Trans. Indus. Inf. 17 (11), 7669–7678 (2021).

Ma, S., Wang, S. & Tsai, W. T. Delay analysis of consensus communication for blockchain-based applications using network calculus. IEEE Wirel. Commun. Lett. 11 (9), 1825–1829 (2022).

Huawei and IDC, “Embrace Change, Outsmart the Future -- Digital Platform Breaks the Situation of Enterprise Digital Transformation”, https://www.huawei.com/cn/news/2019/3/huawei-digital-platform-white-paper , 2019 (accessed 1 March 2023)

Weibull,. Evolutionary game theory (MIT Press, 1997).

Yue, X. et al. An evolutionary game for the behavior of third-party evaluators in pension public–private partnership incorporating public participation. Sci Rep 13 , 20543. https://doi.org/10.1038/s41598-023-47369-1 (2023).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Liu, W. et al. Smart logistics ecological cooperation with data sharing and platform empowerment: An examination with evolutionary game model. Int. J. Prod. Res. 60 (13), 4295–4315 (2022).

Wei, Y., Chen, X. & Zou, X. Data sharing, enterprise strategy and government supervision and incentive—Based on evolutionary game analysis. Financ. Sci. 4 , 107–120 (2020).

Li, X. An evolutionary game-theoretic analysis of enterprise information security investment based on information sharing platform. Manag. Decis. Econ. 43 (3), 595–606 (2022).

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

Jetzek, T., Avital, M. & Bjorn-Andersen, N. Data-driven innovation through open government data. J. Theor. Appl. Electron. Commer. Res. 9 (2), 100–120 (2014).

Lee, J. W. The data sharing economy and open governance of big data as public good. J. Asian Finance Econ. Bus. 8 (11), 87–96 (2021).

Kenney, M. & Zysman, J. The rise of the platform economy. Issues Sci. Technol. 32 (3), 61 (2016).

Chen, W. & Wang, J. Dependent upgrade: Digital transformation strategy of participants in platform ecosystem. Manag. World 37 (10), 195–214 (2021).

Ramaswamy, V. & Ozcan, K. Brand value co-creation in a digitalized world: An integrative framework and research implications. Int. J. Res. Market. 33 (1), 93–106 (2016).

Tan, B., Anderson, E. G. Jr. & Parker, G. G. Platform pricing and investment to drive third-party value creation in two-sided networks. Inf. Syst. Res. 31 (1), 217–239 (2020).

Pfeffer, J. & Salancik, G. The external control of organizations: A resource dependence perspective (Harper and Row, 1978).

Hsu, C. C., Kannan, V. R., Tan, K. C. & Keong Leong, G. Information sharing, buyer-supplier relationships, and firm performance: A multi-region analysis. Int. J. Phys. Distrib. Logist. Manag. 38 (4), 296–310 (2008).

Bessen, J., Impink, S. M., Reichensperger, L. & Seamans, R. The role of data for AI startup growth. Res. Policy 51 (5), 104513 (2022).

Tirole, J. Market failures and public policy. Am. Econ. Rev. 105 (6), 1665–1682 (2015).

OECD. The digital transformation of SMEs OECD studies on SMEs and entrepreneurship (OECD Publishing, 2021).

Book   Google Scholar  

Jeon, D. S., Kim, B. C. & Menicucci, D. Second-degree price discrimination by a two-sided monopoly platform. Am. Econ. J. Microecon. 14 (2), 322–369 (2022).

Bond, P. & Goldstein, I. Government intervention and information aggregation by prices. J. Finance 70 (6), 2777–2812 (2015).

Jaiman, V., Pernice, L. & Urovi, V. User incentives for blockchain-based data sharing platforms. Plos one 17 (4), e0266624 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chen, I. J. & Popovich, K. Understanding customer relationship management (CRM): People, process and technology. Bus. Process Manag. J. 9 (5), 672–688 (2003).

Wang, X., Gu, C., He, Q. & Zhao, J. Game dynamics of tripartite evolution in supply chain finance and credit market. Operat. Res. Manag. 31 (1), 30 (2022).

Lyapunov, A. M. The general problem of the stability of motion. Int. J. Control 55 (3), 531–534 (1992).

Article   MathSciNet   Google Scholar  

The China academy of information and communication, the medium enterprise financing innovation typical model case set, https://www.miit.gov.cn/jgsj/qyj/gzdt/art/2022/art_4e1fefb7ffbc4e3ea5bf8f02c750ab56.html , 2022 (accessed 2 March 2023)

Kingdee, “the 2022 annual performance”, https://www.kingdee.com/newlist/58070,2023 (accessed 22 March 2023)

People’s government of dongguan city, dongguan enterprise digital development report (2021), http://www.dg.gov.cn/gkmlpt/content/3/3624/post_3624587.html#703 , 2021 (accessed 23 March 2023)

The ministry and departments, “about to carry out the pilot work of the transformation of financial support to small and medium-sized enterprise digital notice, http://www.gov.cn/zhengce/zhengceku/2022-08/17/content_5705771.htm , 2022 (accessed 25 March 2023)

IBM, IBM data reveal that cost 2022, https://www.ibm.com/cn-zh/reports/data-breach , 2022 (accessed 25 March 2023)

National bureau of statistics (China), statistics on the medium and tiny enterprise classification method (2017), http://www.stats.gov.cn/tjgz/tzgb/201801/t20180103_1569254.html , 2017 (accessed 26 March 2023)

Wang, X. & Xiao, H. Promoting synergistic development of state-owned enterprises and private enterprises: Progress, problems and countermeasures. Econ. Syst. Reform 05 , 5–13 (2022).

Li, C., Li, H. & Tao, C. Evolutionary game of platform enterprises, government and consumers in the context of digital economy. J. Bus. Res. 167 , 113858 (2023).

The MathWorks, Inc. (September 7, 2016). MATLAB R2016b (Version 9.1.0.441655). Zenodo. https://doi.org/10.5281/zenodo.10983389

Stefansson, G. Business-to-business data sharing: A source for integration of supply chains. Int. J. Prod. Econ. 75 (1–2), 135–146 (2002).

Lin, F. J. & Lai, C. Key factors affecting technological capabilities in small and medium-sized Enterprises in Taiwan. Int. Entrep. Manag. J. 17 (1), 131–143 (2021).

Jiang, S., Cao, J., Wu, H., Chen, K. & Liu, X. Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems. Inf. Sci. 635 , 72–85 (2023).

Cheng, L., Liu, F. & Yao, D. Enterprise data breach: Causes, challenges, prevention, and future directions. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7 (5), e1211 (2017).

Download references

Acknowledgements

This work was supported by The Education Department of Liaoning Province (Grant Number: JYTMS20230826).

Author information

Authors and affiliations.

School of Business Administration, Liaoning Technical University, Xingcheng, 125100, China

Dan Li & Xudong Mei

You can also search for this author in PubMed   Google Scholar

Contributions

Dan Li wrote the main manuscript text, and Xudong Mei built the model and conducted simulation experiments.

Corresponding author

Correspondence to Xudong Mei .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Li, D., Mei, X. Evolutionary game analysis of data sharing among large and medium-sized enterprises in the perspective of platform empowerment. Sci Rep 14 , 11447 (2024). https://doi.org/10.1038/s41598-024-62156-2

Download citation

Received : 04 July 2023

Accepted : 14 May 2024

Published : 20 May 2024

DOI : https://doi.org/10.1038/s41598-024-62156-2

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

• Digital platform
• Enterprises
• Data sharing
• Digital economy
• Evolutionary game

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.