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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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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.

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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 ...

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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

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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.

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.

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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.

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Overview of the Scientific Method

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Your partner for better health, hypothesis in research: definition, types and importance .

April 21, 2020 Kusum Wagle Epidemiology 0

Table of Contents

What is Hypothesis?

• Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
• In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.

Different Types of Hypothesis:

1. Simple Hypothesis:

• A Simple hypothesis is also known as composite hypothesis.
• In simple hypothesis all parameters of the distribution are specified.
• It predicts relationship between two variables i.e. the dependent and the independent variable

2. Complex Hypothesis:

• A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.

3. Working or Research Hypothesis:

• A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.

4. Null Hypothesis:

• A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .

5. Alternative Hypothesis:

• An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .

6. Logical Hypothesis:

• A logical hypothesis is a planned explanation holding limited evidence.

7. Statistical Hypothesis:

• A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.

Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:

Major Differences Between Null Hypothesis and Alternative Hypothesis:

Importance of hypothesis:.

• It ensures the entire research methodologies are scientific and valid.
• It helps to assume the probability of research failure and progress.
• It helps to provide link to the underlying theory and specific research question.
• It helps in data analysis and measure the validity and reliability of the research.
• It provides a basis or evidence to prove the validity of the research.
• It helps to describe research study in concrete terms rather than theoretical terms.

Characteristics of Good Hypothesis:

• Should be simple.
• Should be specific.
• Should be stated in advance.

References and For More Information:

https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf

https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance

https://stattrek.com/hypothesis-test/hypothesis-testing.aspx

http://davidmlane.com/hyperstat/A2917.html

https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html

https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html

https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics

• Characteristics of Good Hypothesis
• complex hypothesis
• example of alternative hypothesis
• example of null hypothesis
• how is null hypothesis different to alternative hypothesis
• Importance of Hypothesis
• null hypothesis vs alternate hypothesis
• simple hypothesis
• Types of Hypotheses
• what is alternate hypothesis
• what is alternative hypothesis
• what is hypothesis?
• what is logical hypothesis
• what is null hypothesis
• what is research hypothesis
• what is statistical hypothesis
• why is hypothesis necessary

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Chapter 4:  The Role of Theory in Research and Practice

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Introduction, defining theory, purposes of theories.

• Components of Theories
• Theory Development and Testing
• Characteristics of Theories
• Theory, Research, and Practice
• Scope of Theories
• Full Chapter
• Supplementary Content

In the development of questions for research or the use of published studies for evidence-based practice (EBP), we must be able to establish logical foundations for questions so that we can interpret findings. This is the essential interplay between theory and research, each integral to the other for advancing knowledge. Theories are created out of a need to organize and give meaning to a complex collection of individual facts and observations. The purpose of this chapter is to discuss components of theories, mechanisms for developing and testing clinical theories, and how we apply theory to research and the application of evidence.

Scientific theory today deals with the empirical world of observation and experience, and requires constant verification. We use theory to generalize beyond a specific situation and to make predictions about what should happen in other similar situations. Without such explanations, we risk having to reinvent the wheel each time we are faced with a clinical problem.

A theory is a set of interrelated concepts, definitions, or propositions that specifies relationships among variables and represents a systematic view of specific phenomena . 1

Research methods are the means by which we conduct investigations in a reliable and valid way so that we can observe clinical phenomena. But it is theory that lets us speculate on the questions of why and how things work, accounting for observed relationships. It allows us to name what we observe, provide potential explanations, and thereby figure out how we can change things in the future.

Theories have always been a part of human cultures, although not all have been scientific. Philosophy and religion have historically played a significant part in the acceptance of theory. The medieval view that the world was flat was born out of the theory that angels held up the four corners of the earth. Naturally, the men of the day were justified in believing that if one sailed toward the horizon, eventually one would fall off the edge of the earth. In healthcare we are aware of significant modifications to our understanding of the human body, as evidenced in the shift from Galen’s view of “pores” in the heart to Harvey’s theory of circulation. In the middle ages, medical theory was based on a balance among four “humours” (blood, black bile, yellow bile, and phlegm) and patients were bled and purged, made to vomit, and made to take snuff to correct imbalances. As theories change, so does our understanding of science and health.

Theories can serve several purposes in science and clinical practice, depending on how we choose to use them.

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• Open access
• Published: 20 April 2024

Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices

• Wasim ul Rehman   ORCID: orcid.org/0000-0002-9927-2780 1 ,
• Omur Saltik 2 ,
• Faryal Jalil 3 &
• Suleyman Degirmen 4  

Humanities and Social Sciences Communications volume  11 , Article number:  524 ( 2024 ) Cite this article

Metrics details

This study aims to investigate the impact of behavioral biases on investment decisions and the moderating role of COVID-19 pandemic information sharing. Furthermore, it highlights the significance of considering cognitive biases and sociodemographic factors in analyzing investor behavior and in designing agent-based models for market simulation. The findings reveal that these behavioral factors significantly positively affect investment decisions, aligning with prior research. The agent-based model’s outcomes indicate that younger, less experienced agents are more prone to herding behavior and perform worse in the simulation compared to their older, higher-income counterparts. In conclusion, the results offer valuable insights into the influence of behavioral biases and the moderating role of COVID-19 pandemic information sharing on investment decisions. Investors can leverage these insights to devise effective strategies that foster rational decision-making during crises, such as the COVID-19 pandemic.

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Introduction

Coronavirus (COVID-19) is recognized as a significant health crisis that has adversely affected the well-being of global economies (Baker et al. 2020 ; Smales 2021 ; Debata et al. 2021 ). First identified in December 2019 as a highly fatal and contagious disease, it was declared a public health emergency by the World Health Organization (WHO) (WHO 2020 ; Baker et al. 2020 ; Altig et al. 2020 ; Smales 2021 ; Li et al. 2020 ). The outbreak swiftly spread across 31 provinces, municipalities, and autonomous regions in China, eventually evolving into a severe global pandemic that significantly impacted the global economy, particularly equity markets and social development (WHO 2020 ; Kazmi et al. 2020 ; Li et al. 2020 ). Since the early 2020 emergence of COVID-19 symptoms, the pandemic has caused considerable market decline and volatility in stock returns, significantly impacting the prosperity of world economies (Rahman et al. 2022 ; Soltani et al. 2021 ; Rubesam and Júnior 2022 ; Debata et al. 2021 ; Baker et al. 2020 ; Altig et al. 2020 ). This situation has garnered the attention of many policymakers and economists since its classification as a public health emergency.

Pakistan’s National Command and Operation Centre reported its first two confirmed COVID-19 cases on February 26, 2020. Following this, the Pakistan Stock Exchange experienced a significant downturn, losing 2266 points and erasing Rs. 436 billion in market equity. Foreign investment saw a notable decline, with stocks worth $22.5 million contracting sharply. By the end of February 2020, stock investments totaling $56.40 million had been liquidated. This dramatic drop in equity markets is attributed to the global outbreak of the COVID-19 pandemic (Khan et al. 2020 ). Additionally, for the first time in 75 years, Pakistan’s economy underwent its most substantial contraction in economic growth, recording a GDP growth rate of −0.4% in the first nine months. All three sectors of the economy—agriculture, services, and industry—fell short of their growth targets, culminating in a loss of one-third of their revenue. Exports declined by more than 50% due to the pandemic. Economists have raised concerns about a potential recession as the country grapples with virus containment efforts (Shafi et al. 2020 ; Naqvi 2020 ). Consequently, the rapid spread of COVID-19 has heightened volatility in financial markets, inflicted substantial losses on investors, and caused widespread turmoil in financial and liquidity markets globally (Zhang et al. 2020 ; Goodell 2020 ; Al-Awadhi et al. 2020 ; Ritika et al. 2023 ). This uncertainty has been exacerbated by an increasing number of positive COVID-19 cases.

Since the magnitude of the COVID-19 outbreak became evident, capital markets worldwide have been experiencing significant declines and volatility in stock returns, affected by all new virus variants despite their effective treatments (Hong et al. 2021 ; Rubesam and Júnior 2022 ; Zhang et al. 2020 ). Previous studies have characterized COVID-19 as a particularly devastating and deadly pandemic, severely impacting socio-economic infrastructures globally (Fernandes 2020 ). The pandemic has disrupted trade and investment activities, leading to imbalances in equity market returns (Xu 2021 ; Shehzad et al. 2020 ; Zaremba et al. 2020 ; Baig et al. 2021 ). In response to the COVID-19 outbreak, various governments, including Pakistan’s, have implemented unprecedented and diverse measures. These include restricting the mobility of the general public and commercial operations, and implementing smart or partial lockdowns, all aimed at mitigating the pandemic’s impact on global economic growth (Rubesam and Júnior 2022 ; Zaremba et al. 2020 ).

Investment decisions become notably complex and challenging when influenced by behavioral biases (Pompian 2012 ). In this context, numerous studies have sought to reconcile various behavioral finance theories with the notion of investors as rational decision-makers. One prominent theory is the Efficient Market Hypothesis, which asserts that capital markets are efficient when decisions are informed by symmetrical information among participants (Fama 1991 ). Yet, in reality, individual investors often struggle to make rational investment choices (Kim and Nofsinger 2008 ), as their decisions are significantly swayed by behavioral biases, leading to market inefficiencies. These biases, including investor sentiment, overconfidence, over/underreaction, and herding behavior, are recognized as widespread in human decision-making (Metawa et al. 2018 ). Prior research has identified various behavioral and psychological biases—such as loss aversion, anchoring, heuristic biases, and the disposition effect—that cause investors to stray from rational investment decisions. Moreover, investors’ responses to COVID-19-related news, like infection rates, vaccine developments, lockdowns, or economic forecasts, often reflect behavioral biases such as investor sentiment, overconfidence, over/underreaction, or herding behavior towards short-term events, thereby affecting market volatility (Soltani and Boujelbene 2023 ; Dash and Maitra 2022 ). These biases may have a wide applicability across different markets, regardless of specific cultural or regulatory differences. Consequently, we posit that these four behavioral biases, in the context of COVID-19, are key factors in reducing vulnerability in investment decisions (Dermawan and Trisnawati 2023 ), especially for individual investors who are more susceptible than in a typical investment environment (Botzen et al. 2021 ; Talwar et al. 2021 ). Therefore, understanding these behavioral biases—such as investor sentiment, overconfidence, over/underreaction, or herding behavior—during the COVID-19 pandemic is crucial, as no previous epidemic has demonstrated such profound impacts of behavioral biases on investment decisions (Baker et al. 2020 ; Sattar et al. 2020 ).

Numerous studies have explored the impact of behavioral biases, including investor sentiment, overconfidence, over/under-reaction, and herding behavior, on investment decisions (Metawa et al. 2018 ; Menike et al. 2015 ; Nofsinger and Varma 2014 ; Qadri and Shabbir 2014 ; Asaad 2012 ; Kengatharan and Kengatharan 2014 ). Recent literature has also shed light on the effects of the COVID-19 pandemic on financial and precious commodity markets (Gao et al. 2023 ; Zhang et al. 2020 ; Corbet et al. 2020 ; Baker et al. 2020 ; Mumtaz and Ahmad 2020 ; Ahmed et al. 2022 ; Hamidon and Kehelwalatenna 2020 ). However, academic research specifically addressing the moderating role of COVID-19 pandemic information sharing on behavioral biases remains limited. It has been observed that global pandemics, such as the Ebola Virus Disease (EVD) and Severe Acute Respiratory Syndrome (SARS), significantly influence stock market dynamics, sparking widespread fear among investors and leading to market uncertainty (Del Giudice and Paltrinieri 2017 ; He et al. 2020 ). This study contributes to the field by examining how behavioral biases, such as investor sentiment, overconfidence, over/under-reaction, and herding behavior, are influenced by the unique circumstances of the COVID-19 crisis. Furthermore, this research provides novel insights into real-time investor behavior and policymaking, thus advancing the academic debate on the role of COVID-19 pandemic information sharing within behavioral finance.

The primary goal of this study is to explore the impact of the COVID-19 crisis on behavioral biases and their effect on investment decisions. Additionally, it aims to assess how various socio-demographic factors influence investment decision-making. These factors include age, occupation, gender, educational qualifications, type of investor, investment objectives, reasons for investing, preferred investment duration, and considerations prior to investing, such as the safety of the principal, risk level, expected returns, maturity period, and sources of investment advice. We hypothesize that these factors significantly influence investment decisions, and our analysis endeavors to investigate the relationship between these factors and investment behavior. By thoroughly examining these variables, the study aims to shed light on the role socio-demographic factors play in investment behavior and enhance the understanding of the investment decision-making process. Additionally, the study seeks to conduct a cluster analysis to identify hierarchical relationships and causality, alongside an agent-based learning model that illustrates the susceptibility of low-income and younger age groups to herding behavior. The article provides the codes and outcomes of the model.

The study will commence with an introduction that outlines the scope and significance of the research. Following this, a literature review will be provided, along with the development of hypotheses concerning the behavioral biases affecting investment decisions and the role of socio-demographic factors in shaping investment behavior. The methodology section will detail the research approach, data collection process, variables considered for analysis, and the statistical methods applied. Subsequently, the results section will present findings from the regression and moderating analyses, cluster analysis, and the agent-based learning model. This will include a detailed explanation of the model codes and their interpretations. The discussion section will interpret the study’s results, highlighting their relevance to policymakers, financial advisors, and individual investors. The article will conclude by summarizing the main discoveries and offering suggestions for further inquiry in this domain.

Literature review and development of hypotheses

Invsetor sentiments and investment decisions.

Pandemic-driven sentiments play a crucial role in determining market returns, making it imperative to understand pandemic-related sentiments to predict future investor returns. Consequently, we posit that the sharing of COVID-19 pandemic information is a critical factor influencing investor sentiments towards investment decisions (Li et al. 2021 ; Anusakumar et al. 2017 ; Zhu and Niu 2016 ; Jiang et al. 2021 ). Generally, investors’ sentiments refer to their beliefs, anticipations, and outlooks regarding future cash flows, which are significantly influenced by external factors (Baker and Wurgler 2006 ). Ding et al. ( 2021 ) define investor sentiment as the collective attitude of investors towards a particular market or security, reflected in trading activities and price movements of securities. A trend of rising prices signals bullish sentiments, while decreasing prices indicate bearish investor sentiment. These sentiments, including emotions and beliefs about investment risks, notably affect investors’ behavior and yield (Baker and Wurgler 2006 ; Anusakumar et al. 2017 ; Jansen and Nahuis 2003 ). Sentiment reacts to stock price news (Mian and Sankaraguruswamy 2012 ), with stock prices responding more positively to favorable earnings news during periods of high sentiment than in low sentiment periods, and vice versa. This sentiment-driven reaction to share price movements is observed across all types of stocks (Mian and Sankaraguruswamy 2012 ). Furthermore, research indicates that market responses to earnings announcements are asymmetrical, especially in the context of pessimistic investor sentiments (Jiang et al. 2019 ). Such reactions were notably pronounced during COVID-19 pandemic news, where sentiments such as fear, greed, or optimism significantly influenced market dynamics (Jiang et al. 2021 ). Thus, information related to the COVID-19 pandemic emerges as a valuable resource for forecasting future returns and market volatility, ultimately affecting investment decision-making (Debata et al. 2021 ).

Overconfidence and investment decision

Standard finance theories suggest that investors aim for rational decision-making (Statman et al. 2006 ). However, their judgments are often swayed by personal sentiments or cognitive errors, leading to overconfidence (Apergis and Apergis 2021 ). Overconfidence in investing can be described as an inflated belief in one’s financial insight and decision-making capabilities (Pikulina et al. 2017 ; Lichtenstein and Fischhoff 1977 ), or a tendency to overvalue one’s skills and knowledge (Dittrich et al. 2005 ). This results in investors perceiving themselves as more knowledgeable than they are (Moore and Healy 2008 ; Pikulina et al. 2017 ).

Overconfidence has been categorized into overestimation, where investors believe their abilities and chances of success are higher than actual, and over-placement, where individuals see themselves as superior to others (Moore and Healy 2008 ). Such overconfidence affects investment choices, leading to potentially inappropriate high-risk investments (Pikulina et al. 2017 ). Overconfident investors often attribute success to personal abilities and failures to external factors (Barber and Odean 2000 ; Tariq and Ullah 2013 ). Overconfidence also leads to suboptimal decision-making, especially under uncertainty (Dittrich et al. 2005 ).

Behavioral finance research shows that individual investors tend to overestimate their chances of success and underestimate risks (Wei et al. 2011 ; Dittrich et al. 2005 ). Excessive overconfidence prompts over-investment, whereas insufficient confidence causes under-investment; moderate confidence, however, leads to more prudent investing (Pikulina et al. 2017 ). The lack of market information often triggers this scenario (Wang 2001 ). Amidst recent market anomalies, COVID-19 information has significantly impacted investors’ overconfidence in their investment decisions. Studies have shown that overconfident investors underestimate their personal risk of COVID-19 compared to the general risk perception (Bottemanne et al. 2020 ; Heimer et al. 2020 ; Boruchowicz and Lopez Boo 2022 ; Druica et al. 2020 ; Raude et al. 2020 ). Overconfidence may lead to adverse selection and undervaluing others’ actions, underestimating the likelihood of loss due to inadequate COVID-19 information (Hossain and Siddiqua 2022 ). Consequently, this study hypothesizes that certain exogenous factors, integral to COVID-19 information sharing, may moderate investment decisions in the context of investor overconfidence.

Over/under reaction and investment decision

The Efficient Market Hypothesis (EMH) suggests that investors’ attempts to act rationally are based on the availability of market information (Fama 1998 ; Fama et al. 1969 ; De Bondt 2000 ). However, psychological biases in investors systematically respond to unwelcome news, leading to overreaction and underreaction, thus challenging the notion of market efficiency (Maher and Parikh 2011 ; De Bondt and Thaler 1985 ). Overreaction and underreaction biases refer to exaggerated responses to recent market news, resulting in the overbuying or overselling of securities in financial markets (Durand et al. 2021 ; Spyrou et al. 2007 ). Barberis et al. ( 1998 ) identified both underreaction and overreaction as pervasive anomalies that drive investors toward irrational investment decisions. Similarly, Hirshleifer ( 2001 ) noted that noisy trading contributes to overreaction, which in turn leads to excessive market volatility.

The impact of the COVID-19 outbreak extends far beyond the loss of millions of lives, disrupting financial markets from every angle (Zhang et al. 2020 ; Iqbal and Bilal 2021 ; Tauni et al. 2020 ; Borgards et al. 2021 ). Market reactions have been significantly shaped by COVID-19 pandemic information sharing, affecting investors’ decisions (Kannadas 2021 ). Recent studies have found that investors’ biases in evaluating the precision and predictive accuracy of COVID-19 information can lead to overreactions and underreactions (Borgards et al. 2021 ; Xu et al. 2022 ; Kannadas 2021 ). Furthermore, research documents the growing influence of COVID-19 information sharing on market reactions worldwide, including in the US, Asian, European, and Australian markets (Xu et al. 2022 ; Nguyen et al. 2020 ; Nguyen and Hoang Dinh 2021 ; Naidu and Ranjeeni 2021 ; Heyden and Heyden 2021 ), indicating that market reactions, characterized by non-linear behavior, are driven by investors’ beliefs.

Previous literature has scarcely explored the role of investors’ overreaction and underreaction in decision-making. Recently, emerging research has begun to enrich the literature by examining the moderating role of COVID-19 pandemic information sharing.

Herding behavior and investment decision

According to the assumptions of Efficient Market Hypothesis (EMH), optimal decision-making is facilitated by the availability of market information and stability of stock returns (Fama 1970 ; Raza et al. 2023 ). However, these conditions are seldom met in reality, as decisions are influenced by human behavior shaped by socio-economic norms (Summers 1986 ; Shiller 1989 ). Behavioral finance research suggests that herding behavior plays a significant role in the decline of asset and stock prices, implying that identifying herding can aid investors in making more rational decisions (Bharti and Kumar 2022 ; Jiang et al. 2022 ; Jiang and Verardo 2018 ; Ali 2022 ). Bikhchandani and Sharma ( 2000 ) define herding as investors’ tendency to mimic others’ trading behaviors, often ignoring their own information. It is essentially a group dynamic where decisions are irrationally based on others’ information, overlooking personal insights, experiences, or beliefs (Bikhchandani and Sharma 2000 ; Huang and Wang 2017 ). Echoing this, Hirshleifer and Hong Teoh ( 2003 ) argue that herding is characterized by investment decisions being influenced by the actions of others.

The sharp market declines prompted by events such as the COVID-19 pandemic raise questions about its influence on investors’ herding behaviors (Rubesam and Júnior 2022 ; Mandaci and Cagli 2022 ; Espinosa-Méndez and Arias 2021 ). Christie and Huang ( 1995 ) observed that investor herding becomes more evident during market uncertainties. Hwang and Salmon ( 2004 ) noted that investors are less likely to exhibit herding during crises compared to stable market periods when confidence in future market prospects is higher. The COVID-19 pandemic, as a major market disruptor, necessitates that investors pay close attention to market fundamentals before making investment decisions. Recent studies suggest that an overload of COVID-19 information could lead to irrational decision-making, potentially challenging the EMH by influencing herding behavior (Jiang et al. 2022 ; Mandaci and Cagli 2022 ). This highlights the importance for investors to be aware of market information asymmetry changes, such as those triggered by the COVID-19 outbreak, which could negatively impact their investment portfolios by altering their herding tendencies. This effect may be more pronounced among individual investors than institutional ones (Metawa et al. 2018 ). A yet unexplored area is the extent to which COVID-19 pandemic information sharing amplifies the herding behavior among investors during investment decision-making processes (Mandaci and Cagli 2022 ).

COVID-19 pandemic information sharing moderating the relationship between behavioral biases and investment decisions

Recent research indicates that the COVID-19 pandemic has notably influenced behavioral biases among investors, affecting their decision-making processes (Betthäuser et al. 2023 ; Vasileiou 2020 ). Since the pandemic’s onset, investors have shown increased sensitivity to pandemic-related news or developments, leading to intensified behavioral biases. This heightened sensitivity poses challenges to investors’ abilities to respond effectively. Specifically, information related to economic uncertainty, infection rates, and vaccination progress has shifted investor sentiment regarding risk perception (Gao et al. 2023 ). Additionally, pandemic news has altered the risk perception of overconfident investors, who previously may have underestimated the risks associated with COVID-19 (Bouteska et al. 2023 ). The increased uncertainty and market volatility triggered by COVID-19 news have also prompted investors to adapt their reactions based on new information, potentially fostering more rational decision-making (Jiang et al. 2022 ). The rapid spread of COVID-19-related news has been shown to diminish mimicry in investment decisions (Nguyen et al. 2023 ). This indicates that viral news about the pandemic makes investors more discerning regarding risk perceptions and investment strategies, moving away from mere herd behavior. Based on this discussion, the study proposes that COVID-19 pandemic information sharing acts as a moderating factor in the relationship between behavioral biases and investment decisions.

Sociodemographic factors and investment decision

The influence of demographic factors like gender, age, income, and marital status on investor behavior is well-documented in financial literature. However, examining these relationships within specific geographical contexts—such as countries, regions, states, and provinces—reveals that cultural values, beliefs, and experiences may blur the distinctions between human and cognitive biases in terms of their nuanced impacts. Evidence shows that certain demographic groups, particularly young male investors with lower portfolio values from regions less developed in terms of education and income, are more prone to overconfidence and familiarity bias in their trading activities. Conversely, investors with higher education levels and female investors are inclined to trade less frequently, resulting in better investment returns (Barber and Odean 2000 ; Gervais and Odean 2001 ; Glaser and Weber 2007 ).

This study’s findings further suggest that with increased stock market experience, investors tend to discount emotional factors, leading to more rational investment choices. Nonetheless, experience alone does not appear to markedly influence the decision-making process among investors (Al-Hilu et al. 2017 ; Metawa et al. 2019 ).

In summary, demographic variables such as age, gender, and education significantly impact investment decisions, especially when considered alongside behavioral aspects like investor sentiment, overconfidence, and herd behavior. Gaining insight into these dynamics is crucial for investors, financial advisors, and policymakers to devise effective investment strategies and enhance financial literacy.

Research methodology

Data and sampling.

The research methodology outlines the strategy for achieving the study’s objectives. This research adopted a quantitative approach, utilizing a survey method (questionnaire) to examine the behavioral biases of individual investors in Pakistan during the COVID-19 pandemic. The target population comprised individual investors from Punjab province, specifically those interested in capital investments. Data were collected through convenient sampling techniques. A total of 750 questionnaires were distributed via an online survey (Google Form) to investors in four major cities of Punjab province: Karachi, Lahore, Islamabad, and Faisalabad. Initially, 257 respondents completed the survey following follow-up reminder emails. Out of these, 223 responses were deemed usable, yielding a valid response rate of 29.73% for further analysis (Saunders et al. 2012 ).

To mitigate potential biases during the data collection process, we conducted analyses for non-response and common method biases. Non-response bias, which arises when there is a significant difference between early and late respondents in a survey, was addressed by comparing the mean scores of early and late respondents using the independent samples t -test (Armstrong and Overton 1977 ). Results (see Table 1 ) indicated no statistically significant ( p  > 0.05) difference between early and late responses, suggesting that response bias was not a significant issue in the dataset.

Furthermore, to assess the potential threat of common method variance, we applied Harman’s single-factor test, a widely used method to evaluate common method biases in datasets (Podsakoff et al. 2003 ). This technique is aimed at identifying systematic biases that could compromise the validity of the scale. Through exploratory factor analysis (EFA) conducted without rotation, it was determined that no single factor accounted for a variance greater than the threshold (i.e., 50%). Consequently, common method variance was not considered a problem in the dataset, ensuring the reliability of the findings.

Figure 1 illustrates the framework of the model established for regression and moderating analyses that reveal the interactions between behavioral biases, investment decisions and COVID-19 pandemic information sharing.

Covid-19 pandemic informing sharing.

Measures for behavioral biases

A close-ended questionnaire based on five-point Likert measurement scales was prepared scaling (1= “strongly disagree” to 5= “strongly agree”) to operationalize the behavioral biases of investors. The first predictor is investor sentiments. It refers to investors’ beliefs and perspectives related to future cash flows or discourses of specific assets. It is a crucial behavioral factor that often drives the market movements, especially during pandemic. We used the modified 5-items scale from the study of (Metawa et al. 2018 ; Baker and Wurgler 2006 ). Second important behavioral factor is overconfidence, which measured the tendency of decision-makers to unwittingly give excessive weight to the judgment of knowledge and correctness of information possessed and ignore the public information (Lichtenstein and Fischhoff 1977 ; Metawa et al. 2018 ). This construct was measured by using the 3-items scale developed by Dittrich et al. ( 2005 ). In line with the studies of (see for example (De Bondt and Thaler 1985 ; Metawa et al. 2018 ), we opted the 4-items scale to measure the over/under reactions. It illustrates that investors systematically overreact to unexpected news, and this leads to the violation of market efficiency. They conclude that investors attach great importance to past performance, ignoring trends back to the average of that performance (Boubaker et al. 2014 ). Last, herding behavior effect means theoretical set-up suggesting that investment managers are imitating the strategy of others despite having exclusive information. Such managers prefer to make decisions according to the connected group to avoid the risk of reputational damage (Scharfstein and Stein 1990 ). In sense, a modified scale was anchored to examine the herd behavior of investors from the studies of Bikhchandani and Sharma ( 2000 ) and Metawa et al. ( 2018 ).

Measures for COVID-19 pandemic information sharing

To assess the moderating effect of COVID-19 pandemic information sharing, it was examined in terms of uncertainty, fear, and perceived risk associated with the virus (Kiruba and Vasantha 2021 ). Previous studies indicate that COVID-19 news and developments have markedly affected the behavioral biases of investors (Jiang et al. 2022 ; Nguyen et al. 2023 ). To this end, an initial scale was developed to measure the moderating effect of COVID-19 pandemic information sharing. The primary reason for creating a new scale was that existing scales lacked clarity and were not specifically designed to assess how anchoring behavioral biases affect investment decisions. Subsequently, a self-developed scale was refined with input from a panel of experts, including two academicians specializing in neuro or behavioral finance and two investors with expertise in the capital market, to ensure the scale’s face and content validity regarding COVID-19 pandemic information sharing. They reviewed the scale in terms of format, content, and wording. Based on their comprehensive review, minor modifications were made, particularly aligning the scale with pandemic news and developments to accurately measure the impact of the COVID-19 health crisis on investors’ behavioral biases. Ultimately, a four-item scale, employing a five-point Likert scale (1= “strongly disagree” to 5= “strongly agree”), focusing on COVID-19 related aspects (e.g., infection rates, lockdowns, vaccine development, and government stimulus packages) was utilized to operationalize the construct of COVID-19 pandemic information sharing (Bin-Nashwan and Muneeza 2023 ; Li and Cao 2021 ).

I believe that increasing information about rate of COVID-19 infections influenced my investment decisions.

I believe that increasing information about COVID-19 lockdowns influenced my investment decisions.

I believe that increasing information about COVID-19 vaccinations development, influenced my investment decisions, and

I believe that increasing information about government stimulus packages influenced my investment decisions.

Measures for investment decisions

To measure investment decision, the modified five points Likert scale ranging from (1= “strongly disagree” to 5= “strongly agree”) has been opted from the study of Metawa et al. ( 2018 ).

Hypotheses of study

The hypotheses of the study regarding regression analysis and moderating analyses are as follows in Table 2 :

The hypotheses outlined above were tested using regression analyses and moderating analyses. To reveal the clustering tendencies of investors exhibiting similar behaviors, cognitive biases, and sociodemographic variables, the feature importance values were investigated using K-means clustering analyses. Furthermore, findings and recommendations were provided to policymakers using agent-based models to develop policy suggestions within the scope of these hypotheses, offering insights for academic purposes.

Demographic profile of respondents

Table 3 provides a brief demographic profile of respondents.

Based on the percentages presented in Table 3 , the study primarily focuses on a specific demographic profile. Most participants were 20–30 years old (61.0%) with a higher educational background, particularly a master’s degree (67.3%). They were mostly salaried individuals (56.5%), male (61.0%), and identified as seasonal investors (63.7%). The investment objective of this group was mostly focused on growth and income (37.2%), while wealth creation (41.3%) was their primary purpose for investing. They preferred to invest equally in medium-term (43.5%) and long-term (28.3%) periods and considered high returns (38.6%) as the primary factor before investing. They received investment advice primarily from family and friends (44.8%) and social media (29.6%). Overall, the study indicates that the sample consisted of younger, male, salaried individuals with higher education levels who rely on personal networks and social media for investment advice. Their investment objectives are focused on wealth creation through growth and income, with an equal preference for medium and long-term investments.

Analysis and results

Descriptive summary.

Table 4 outlines the measures used to evaluate the constructs of the study, detailing the number of items for each construct, mean values, standard deviations, zero-order bivariate correlations among the variables, and Cronbach’s Alpha values. The evaluation encompasses a total of 29 items spread across six constructs: investor sentiments (5 items), overconfidence (3 items), over/under reaction (4 items), herding theory (3 items), investment decision (10 items), and COVID-19 information impact (4 items). The mean scores for these items fall between 3.535 and 3.779, with standard deviations ranging from 0.877 to 0.965.

Parallel coordinates (see Figs. 2 – 5 ) visualization is employed as a method to depict high-dimensional data on a two-dimensional plane, proving particularly beneficial for datasets with a large number of features or attributes. This technique involves the use of vertical axes to represent each feature, connected by horizontal lines that represent individual data points. This visualization method facilitates the identification of patterns, detection of clusters or outliers, and discovery of correlations among the features. Therefore, parallel coordinates visualization is instrumental in analyzing complex datasets, aiding in the informed decision-making process based on the insights obtained.

Strongly disagree (CIS1) choice parallel coordinates.

Disagree (CIS2) choice parallel coordinates.

Agree (CIS3) choice parallel coordinates.

Strongly agree (CIS4) choice parallel coordinates.

The analysis of responses to the COVID-19 information sharing questions reveals a significant correlation with the second and fourth-level responses concerning cognitive biases, including investor sentiment, overconfidence, over/under reaction, and herding behavior. This observation leads to two key insights. Firstly, participants demonstrate an ability to perceive, respond to, and comprehend the nuances of their investment decisions as related to investor sentiment, overconfidence, over/under reaction, and herding behavior. Consequently, they show a propensity to make clear decisions, indicating agreement or disagreement in their responses. Secondly, it is noted that individuals who acknowledge being significantly influenced by COVID-19 news tend to adopt more balanced investment strategies concerning these cognitive biases. Additionally, younger individuals, particularly those self-employed or not professionally investing, who show a preference for long-term value investments, are more inclined to exhibit these tendencies.

The value of the Pearson correlation coefficient (r) was calculated to investigate the nature, strength and relationship between variables. The results of correlation analysis reveal that all the constructs positively correlated.

To investigate the interconnections among variables in the dataset, correlations were computed and illustrated through a network graph. The correlation matrix’s values served as the basis for edge weights in the graph, with more robust correlations depicted by thicker lines (see Fig. 6a ). Each variable received a unique color, and connections showcasing higher correlations utilized a distinct color scheme to enhance visual clarity. This method offers a graphical depiction of the intricate relationships among various variables, facilitating the discovery of patterns and insights that might remain obscured within a conventional correlation matrix.

a Correlation diagraphs and matrix. b Correlation diagraphs and matrix.

The correlation analysis revealed a pronounced relationship between cognitive biases (such as investor sentiments, overconfidence, herd behavior, and investment decisions), COVID-19 information sharing, and socio-demographic factors (including age group, occupation, gender, educational qualifications, type of investor, investment objectives, investment purposes, preferred investment duration, factors considered prior to investing, and sources of investment advice). A correlation matrix graph was constructed to further elucidate these correlations, assigning different colors to each variable for visual differentiation (see Fig. 6b ). The thickness of the lines in the graph correlates with the strength of the relationships, indicating variables with high correlation more prominently.

These findings underscore the interconnected nature of the study variables, demonstrating that cognitive biases and socio-demographic factors exert a considerable impact on investment decisions. This analytical approach highlights the complexity of investor behavior and underscores the multifaceted influences on investment choices, providing valuable insights for understanding how various factors interact within the investment decision-making process.

Reliability test

For reliability test, the Cronbach alpha values were examined to check the internal consistency of the measure. The internal consistency of an instrument tends to indicate whether a metric or an indicator measure what it is intended to measure (Creswell 2009 ). The Cronbach’s alpha greater than 0.7 indicates that all the items or the questions regarding the respective variable are good, highly correlated and reliable. The calculated Cronbach coefficient value for Investor sentiments (alpha = 0.888), over confidence (alpha = 0.827), over/under reaction (alpha = 0.858), herding behavior theory (alpha = 0.741), Investment decision (alpha = 0.933) and COVID-19 (alpha = 0.782) indicates that all of the constructs are reliable.

Validity test

Validity refers to the extent to which an instrument accurately measures or performs what it is designed to measure (Kothari 2004 ). To ensure the validity of the questionnaire and its constructs, the researcher engaged in a comprehensive literature review, sought the advice of consultants, and incorporated feedback from other professionals in the field. Additionally, the concepts of convergent validity and discriminant validity were evaluated to further assess the instrument’s validity.

Convergent validity assesses the extent to which items that are theoretically related to a single construct are, in fact, related in practice (Wang et al. 2017 ). To determine convergent validity, factor loading, Average Variance Extracted (AVE), and Composite Reliability (CR) were calculated. According to Hair et al. ( 1998 ), factor loading values should exceed 0.60, composite reliability should be 0.70 or higher, and AVE should surpass 0.50 to confirm adequate convergent validity.

Table 5 demonstrates that all constructs utilized in this study surpass these threshold values, indicating strong convergent validity. This suggests that the items within each construct are consistently measuring the same underlying structure, reinforcing the validity of the questionnaire’s design and the constructs it aims to measure.

Discriminant validity measures the degree that the concepts are distinct from each other (Bagozzi et al. 1991 ) and it is evident that if alpha value of a construct is greater than the average correlation of the construct with other variables in model, the existence of discriminant validity exist (Ghiselli et al. 1981 ).

Hypotheses testing

To examine the conditional moderating effect of COVID-19 on the influence of behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) on investment decision-making, moderation analysis was conducted using the Process Macro (Model 1) for SPSS, as developed by Hayes, with bootstrapping samples at 95% confidence intervals. According to Hayes ( 2018 ), the analysis first explores the direct impact of the behavioral factors on investment decisions. Subsequently, it assesses the indirect influence exerted by the moderating variable (COVID-19). This two-step approach allows for a comprehensive understanding of how COVID-19 modifies the relationship between investors’ behavioral biases and their decision-making processes, shedding light on the extent to which the pandemic acts as a moderating factor in these dynamics.

For this study the mathematical model to test moderating role of COVID-19 pandemic information sharing can be explained as:

Y = Investment decisions (Dependent variable)

β 0  = Intercept

X 1  = Investment sentiments (Independent variable)

X 2  = Overconfidence (Independent variable)

X 3  = Over/under reaction (Independent variable)

X 4  = Herding behavior (Independent variable)

β 1 X 1  = Intercept of investors sentiments

β 2 X 2  = Intercept of overconfidence

β 3 X 3  = Intercept of over/under reaction

β 4 X 4  = Intercept of herding behavior

(X 1 * COVID-19) = Investors’ sentiments and moderation effect of COVID-19 information

(X 2 * COVID-19) = Overconfidence and moderation effect of COVID-19 information

(X 3 * COVID-19) = Over/under reaction and moderation effect of COVID-19 information

(X 4 * COVID-19) = Herding behavior and moderation effect of COVID-19 information

μ = Residual term.

Direct effect

In Table 6 , the direct effect of the independent variables on the dependent variable demonstrates that the behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) significantly influence investment decision (ID) with beta values of 0.961, 0.867, 0.884, and 0.698, respectively. The confidence interval (CI) values presented in Table 6 confirm these relationships are statistically significant. The positive and significant outcomes underline that behavioral factors critically impact investors’ decision-making attitudes. Consequently, Hypotheses 1, 2, 3, and 4 (H1, H2, H3, and H4) are accepted, affirming the substantial role of investor sentiments, overconfidence, over/under reaction, and herding behavior in shaping investment decisions.

Indirect moderating effect

In the context of the COVID-19 pandemic and its associated risks, the impact of behavioral factors (investor sentiments, overconfidence, over/under reaction, and herding behavior) on investment decisions tends to diminish. The findings presented in Table 6 and illustrated in Fig. 7 indicate that COVID-19 information sharing significantly and negatively moderates the relationship between these factors and investment decisions, leading to the acceptance of Hypotheses 5, 6, 7, and 8 (H5, H6, H7, and H8). The negative beta values underscore that the presence of COVID-19 adversely influences investors’ behavior, steering them away from rational investment decisions. This demonstrates that the pandemic context acts as a moderating factor, altering how behavioral biases impact investment choices, ultimately guiding investors towards more cautious or altered decision-making processes.

Moderating effect of Covid-19 pandemic information sharing.

K-means clustering analysis

K-means clustering analysis is utilized to uncover natural groupings within datasets by analyzing similarities between observations. This technique is especially beneficial for managing large and complex datasets as it reveals patterns and relationships among variables that may not be immediately evident. In this study, K-means clustering helps identify natural groupings based on socio-demographic factors, cognitive biases regarding investment decisions, and COVID-19 pandemic information sharing, thereby offering insights into the data’s underlying structure and identifying potential patterns or relationships among key variables.

The cluster analysis aims to ascertain the feature importance value of groups with similar investor behaviors, which is crucial for determining agents’ investment functions in subsequent agent-based modeling. Selecting the appropriate number of clusters in the K-means algorithm is essential, yet challenging, as different numbers of clusters can yield varying results (Li and Wu 2012 ).

Two prevalent methods for determining the optimal number of clusters are:

Elbow Method: This approach involves running the K-means algorithm with varying cluster numbers and calculating the total sum of squared errors (SSE) for each. SSE represents the squared distances of each data point from its cluster’s centroid. Plotting the SSE values against the number of clusters reveals a point known as the “elbow,” where the rate of SSE decrease markedly slows, indicating the optimal cluster number (Syakur et al. 2018 ).

Silhouette Analysis: Not mentioned directly in the narrative, but it’s another method that measures how similar an object is to its own cluster compared to other clusters. The silhouette score ranges from −1 to 1, where a high value indicates the object is well matched to its own cluster and poorly matched to neighboring clusters.

The sklearn library provides tools for implementing the elbow method and silhouette analysis. For example, the code snippet described applies the elbow method by varying the number of clusters from 1 to 10 and calculating SSE for each scenario. The optimal number of clusters is identified by selecting a value near the elbow point on the resulting plot.

After clustering, the analysis progresses by using the fit () method from sklearn’s K-Means class to cluster the data, determine each cluster’s center coordinates, and assign each data point to a cluster. Feature importance values can be calculated using the Extra Trees Classifier class from sklearn, and these values can be visualized through a line graph.

Finally, to illustrate the clusters’ membership to the CIS1, CIS2, CIS3, and CIS4 inputs as a color scale bar, the seaborn library is used (see Fig. 8 (top) and Fig. 8 (bottom)). This involves calculating the average membership values for each cluster and visualizing these averages, providing a clear depiction of how each cluster associates with the different inputs, enriching the analysis of investor behaviors and their responses to COVID-19 information sharing.

Elbow method sum of squared error class determination (top) and clustering analysis results (bottom).

After employing a network diagram constructed from a correlation matrix to elucidate the interrelationships among variables, and utilizing the Elbow method to ascertain the optimal number of clusters, the K-means clustering algorithm was applied (see Fig. 9 ). This approach successfully identified three distinct clusters, highlighting the variables that exerted a significant influence on these clusters. Notably, the COVID-19 pandemic information sharing variable, along with its corresponding CIS1, CIS2, CIS3, and CIS4 values, emerged as significant factors. The analysis indicated that overconfidence and overreaction were the predominant factors in crucial clustering, alongside cognitive biases and investment strategies that lead to similar behaviors among investors and varying levels of impact from COVID-19.

Cluster analysis feature importance value results.

Furthermore, sociodemographic factors such as age, occupation, and investor type were also identified as influential determinants. Leveraging these insights, policymakers and researchers can develop an agent-based model that incorporates herd behavior, along with age and income levels categorized by occupation, to effectively simulate market dynamics. This approach facilitates a comprehensive understanding of how different factors, particularly those related to the COVID-19 pandemic, influence investor behavior and market movements, thereby enabling the formulation of more informed strategies and policies.

An ingenious agent-based simulation for herding behavior

In this study, the findings of behavioral economics and finance research may contain results that are easy to interpret for policymakers but may involve certain difficulties in practical implementation. Specifically, for policymakers, an agent-based model has been created (see Appendix 1 for pseudo codes. In case, requested python codes are available). In a model consisting of 223 agents who trade on a single stock, prototypes of investors have been created based on the analysis presented here, and characteristics such as age group and income status, which are relatively easy to access or predict regarding their socio-demographic profiles, have been taken into account in the herd behavior function, considering the decision to follow the group or make independent decisions. Younger and lower-income agents were allowed to exhibit a greater tendency to follow the group, while 50 successful transactions were monitored to determine in which trend of stock price increase or decrease the balance of the most successful agent was increased or decreased (Gervais and Odean 2001 ).

In addressing the influence of age and income status on herding behavior, it is imperative to underscore the nuanced interplay between various socio-economic and psychological factors within our agent-based model framework. The model’s robustness stems from its capacity to simulate a range of investor behaviors by integrating key determinants such as investor sentiment, overconfidence, reaction to market events, and socio-demographic characteristics. Herein we expound on the contributory elements:

Investor Sentiment (IS1–IS5)

The model encapsulates the variability of investor sentiment, which oscillates with age and income, influencing individuals’ financial perspectives and risk propensities. Younger investors’ sentiment may tilt towards optimism driven by a more extensive investment horizon, while lower-income investors’ sentiment could lean towards caution, primarily driven by the pressing requirement for financial dsecurity (Baker and Wurgler 2007 ).

Overconfidence (OF1–OF5)

The tendency towards overconfidence is dynamically modeled, particularly among younger investors who may overrate their market acumen and predictive capabilities. This overconfidence may also manifest among lower-income investors as a psychological compensatory mechanism for resource inadequacy (Malmendier and Tate 2005 ).

Over/Under Reaction (OUR1–OUR5)

The model accounts for the influence of age and income on the velocity and extent of response to market stimuli. Inexperienced or financially restricted investors may be prone to overreactions due to a lack of market exposure or intensified economic strain (Daniel et al. 1998 ).

Herding Behavior (HB1–HB4)

Within the simulated environment, herding is more pronounced among younger investors, possibly due to peer influence, and among lower-income investors who may seek safety in conformity (Bikhchandani et al. 1992 ).

Investment Decision (ID1–ID10)

The model intricately reflects the complexities of investment decisions influenced by age-specific factors such as projected earnings and lifecycle influences. Investors with limited income may exhibit a predilection for security, swaying their investment choices (Yao and Curl 2011 ).

COVID-19 Information Sharing (CIS1–CIS4)

The pandemic era’s nuances are integrated into the model, acknowledging that younger investors could be more susceptible to digitally disseminated information, which, in turn, impacts their investment decisions. The credibility and source of information are also calibrated based on income levels (Shiller 2020 ).

Socio-demographic factors

Age: The model simulates younger investors’ reliance on the conduct of others, utilizing it as a heuristic substitute for experience (Dobni and Racine 2016 ).

Occupation: It captures how occupational background can broaden or restrict access to information and influence herding tendencies (Hong et al. 2000 ).

Gender: Gender disparities are incorporated, reflecting on investment styles where men may be more disposed to herding due to overconfidence (Barber and Odean 2001 ).

Qualification (Qualif.): The model acknowledges that higher education and financial literacy levels can curtail herding by fostering self-reliant decision-making (Lusardi and Mitchell 2007 ).

Investor Type (InvTyp): It differentiates between retail and institutional investors, noting that limited resources might push retail investors towards herding (Nofsinger and Sias 1999 ).

Investment Objective (InvObj): The model recognizes that short-term objectives might amplify herding as investors chase swift gains (Odean 1998 ).

Purpose: It contemplates the conservative herding behavior that is aligned with goals like retirement savings (Yao and Curl 2011 ).

Investment Horizon (Horizon): A lengthier investment horizon is modeled to potentially dampen herding tendencies (Kaustia and Knüpfer 2008 ).

Factors Considered Before Investing (factors): The model simulates a range of investment considerations, including risk tolerance and expected returns, which influence herding propensities (Shefrin and Statman 2000 ).

Source of Investment Advice (source): The influence of advice sources, such as analysts or financial media, on herding is also captured within the model (Tetlock 2007 ).

In conclusion, the agent-based model we present is meticulously designed to reflect the intricate fabric of financial market behavior. It is particularly attuned to the multi-layered aspects that drive herding, informed by empirical evidence and theoretical underpinnings that rigorously define the interrelations between investor demographics and market behavior. The aforementioned socio-economic and psychological facets provide a comprehensive backdrop against which the validity and consistency of the model are substantiated.

The following code has been prepared using Python programming language with the Mesa, Pandas, SciPy, NumPy, Random and Matplotlib libraries. This code simulates a herd behavior of stock traders in a simple market (Hunt and Thomas 2010 ; McKinney 2010 ; Harris et al. 2020 ; Virtanen et al. 2020 ; Van Rossum 2020 ; Hunter 2007 ). The simulation runs for 50-time steps, with the stock price and balance of each agent printed at each step. The decision-making process of agents in the simulation is stochastic, with agents randomly choosing to buy, sell, or follow the market trend based on their characteristics and decision-making strategy.

The Stock Trader class in the model symbolizes individual agents, each characterized by a unique ID, balance, and a stock price. These agents are equipped with a method to compute the current stock price. The step() function within each agent embodies their decision-making process, which is influenced by their current balance and the prevailing stock price. Agents have the option to buy, sell, or align with the market trend, reflecting various investment strategies.

The Herding Model class encapsulates the entire simulation framework. It generates a population of Stock Trader agents and progresses the simulation over a designated number of time steps. Within this class, the agent_decision() method orchestrates each agent’s decision-making, factoring in individual characteristics and strategies. The step() method, in turn, adjusts the stock price based on the aggregate current stock prices of all agents before executing the step() method for each agent, thereby simulating the dynamic nature of the stock market.

Socio-demographic factors, specifically age and income status, are integrated into the agent-based model simulations, drawing upon insights from Parallel Coordinates and Cluster Analysis as well as relevant literature. The simulation posits that agents of younger age and lower income are predisposed to mimicking the market trend, whereas other agents exhibit a propensity for independent decision-making. Given the stochastic nature of the decision-making process, the behavior of agents varies across different runs of the simulation, introducing an element of unpredictability.

At each time step, the simulation outputs the stock price and balance of each agent, offering a snapshot of the market dynamics at that moment. Figure 10 provides a flow diagram elucidating the operational framework of the model’s code, presenting a visual representation of how the simulation unfolds over time.

Flowchart of agent-based model.

This model architecture allows for the exploration of how socio-demographic characteristics influence investment behaviors within a simulated market environment, offering valuable insights into the mechanisms driving market trends and individual investor decisions.

Within our agent-based model (ABM), “performance” embodies multiple dimensions reflective of the agents’ investment outcomes, influenced by socio-demographic factors and behavioral biases. The provided pseudo-code conceptualizes the implementation of these facets in the model.

Metrics used to quantify agent performance

Balance trajectory.

This primary indicator tracks the evolution of each agent’s financial balance over time, reflecting the impact of their buy, sell, or market trend-following decisions (Arthur 1991 ).

Decision strategy efficacy

Evaluates the effectiveness of an agent’s decision-making strategy (‘buy’, ‘sell’, or ‘follow’), influenced by socio-demographic variables such as age and income, as delineated in the agent_decision method (Tesfatsion and Judd 2006 ).

Market trend alignment

Assesses the correlation between an agent’s balance trajectory and overall market trends, indicating successful performance if an agent’s balance increases with market prices (Shiller 2003 ).

Risk management

Infers risk management skill from the volatility of balance changes, with less volatility indicating stable and potentially successful investment strategies (Markowitz 1952 ).

Wealth accumulation

Agents are ranked by their final balance at the simulation’s end to identify the most financially successful outcomes (De Long et al. 1990 ).

Adaptive behavior

The model evaluates agents’ adaptability to market price changes, revealing their capacity to capitalize on market movements (Gode and Sunder 1993 ).

Herding influence

Considers how herding behavior impacts financial outcomes, especially for younger and lower-income agents as programmed in the Herding Model class (Bikhchandani et al. 1992 ).

These performance metrics are quantified through agents’ balance and stock price histories, updated at each simulation step. These histories offer a time series analysis of financial trajectories, enabling pattern identification such as herding tendencies or the effects of overconfidence.

The model’s realism is enhanced by parameters like young_follow_factor and low_income_follow_factor, adjusting the propensity for herding among different socio-demographic groups. This inclusion allows the model to reflect real-world dynamics where age and income significantly impact investment performance.

In conclusion, our ABM presents a detailed framework for examining investment performance’s complex nature. It integrates behavioral economics and socio-demographic data, providing insights into investor behavior under simulated market conditions.

Characteristics of agents in the agent-based model

Demographics (age and income): Consistent with the focus of our study on socio-demographic factors, each agent is characterized by age and income parameters, which influence their investment behavior, particularly their propensity towards herding. Age and income are randomly assigned within realistic bounds reflecting the demographic distribution of typical investor populations.

Cognitive biases: Agents are imbued with behavioral attributes such as overconfidence, herding instinct, and over/under-reaction tendencies to market news, reflecting the psychological dimensions of real-world investors.

Investment strategy: Each agent follows a distinct investment strategy categorized broadly as ‘buy’, ‘sell’, or ‘follow’ (herding). The strategy is influenced by the agent’s demographic characteristics and cognitive biases.

Adaptability: Agents are capable of learning and adapting to market changes over time, simulating the dynamic and evolving nature of real-world investor behavior.

Social influence: Agents are influenced by other agents’ behaviors, especially under conditions conducive to herding, modeling the social dynamics of investment communities.

Wealth and portfolio: Agents have a variable representing their wealth, which fluctuates based on investment decisions and market performance. Their portfolio composition and changes therein are also tracked, offering insights into their risk-taking and diversification behaviors.

Significance of agent-based modeling

Agent-based modeling is a powerful tool that allows researchers to simulate and analyze complex systems composed of interacting agents. Its significance and utility in various fields, including economics and finance are profound:

Complexity and emergence: ABM can capture the emergent phenomena that arise from the interactions of many individual agents, providing insights into complex market dynamics that are not apparent at the individual level (Epstein and Axtell 1996 ).

Customizability and scalability: ABMs can be tailored to include various levels of detail and complexity, allowing for the simulation of systems ranging from small groups to entire markets (Tesfatsion and Judd 2006 ).

Experimental flexibility: ABMs facilitate virtual experiments that would be impractical or impossible in the real world, enabling researchers to explore hypothetical scenarios and policy implications (Gilbert and Troitzsch 2005 ).

Realism in behavioral representation: By incorporating cognitive biases and decision-making rules, ABMs can realistically represent human behavior, providing deeper behavioral insights than models assuming perfect rationality (Hommes 2006 ).

Policy analysis and forecasting: In economics and finance, ABMs are particularly useful for policy analysis, risk assessment, and forecasting, as they can incorporate a wide range of real-world factors and individual behaviors (LeBaron and Tesfatsion 2008 ).

By integrating these agent characteristics into our ABM and considering the broader implications of agent-based modeling, our study aims to provide nuanced insights into herding behavior among investors. We believe that our approach not only aligns with best practices in the field but also significantly contributes to the understanding of complex investment behaviors and market dynamics. We trust that this expanded description addresses the reviewer’s comment and underscores the robustness and relevance of our agent-based simulation approach.

Figure 11a, b panels display the balance changes of agents with respect to stock prices, age, and income status. By coding the balance increases and decreases as +1 and −1, respectively, and employing a line graph that matches the changes in stock prices, it has become possible to provide information about the agents’ performance. In panels a and b, it is observed that agents created after the age of 37.5 have been included in the higher income group on average, and during transitions of stock prices below 12.75 units, between 17 and 20 units, and between 26 and 27.50 units, the agents’ responses to price state changes are accompanied by noticeable transitions (increases and decreases) in their portfolio states, depending on age and income status.

a Agents’ performance. b Agents’ responses.

In Fig. 12 , in the agent-based model’s 50 repeated simulations, at the 45th simulation, the stock price is 20.03 units, and the balance of agent number 74 reaches 911 units. The price-income-balance change graph for the agent throughout the 50 transactions is presented below.

Balance change according to stock price for agent 74.

Upon examining the descriptive statistics of the income for agent number 74, who diverges from the herding tendency profile of the model and is in the higher income group aged 40 and above, the highest balance value is 911 units, the lowest balance level is 732 units, the average is 799 units, and the standard deviation is 41 units. When the overall balance of the agents is investigated, it is observed that the average balance of the agents is around 84 units. Considering the existence of an agent with the lowest balance of −670 units, it can be concluded that agent number 74 has demonstrated a significantly superior performance.

Discussion and conclusion

The influence of behavioral biases on investors’ decision-making has yielded mixed findings in literature. Wan ( 2018 ) observed a positive impact of behavioral biases, considered forward-looking factors, on investment decisions. Conversely, Zulfiqar et al. ( 2018 ) noted a markedly negative impact of overconfidence on investment decisions. Similarly, Aziz and Khan ( 2016 ) explored the role of heuristic factors (representative, anchoring, overconfidence, and availability bases) and found them significantly influencing investment decision and performance. However, they reported that prospect factors (loss aversion, regret aversion, and mental accounting biases) had an insignificant impact on these outcomes.

These varied results may stem from a complex interplay of factors such as cultural differences, pandemic-related information, economic conditions, regulatory environments, historical context, and investors’ financial literacy levels, contributing to differences in how behavioral biases influence investment decisions across regions (Metawa et al. 2018 ).

This study contributes to the field of behavioral finance by revealing the moderating role of COVID-19 pandemic information sharing on the relationship between behavioral quirks and investment choices, specifically in the context of Pakistan. Key contributions include:

Investors’ sentiments

This study shows that COVID-19 pandemic information sharing significantly moderates the relationship between investors’ sentiments and their investment decisions, validating that pandemic-related information, such as infection rates and economic downturns, heavily influences investors’ sentiments and alters their risk perceptions (Anastasiou et al. 2022 ; Hsu and Tang 2022 ; Bin-Nashwan and Muneeza 2023 ; Gao et al. 2023 ; Sohail et al. 2020 ).

Overconfidence

It reveals how COVID-19 information reshapes overconfident investors’ risk perceptions, urging them to reassess their investment portfolios in light of the pandemic’s uncertainties and economic implications (Bouteska et al. 2023 ; Li and Cao 2021 ).

Over/under reaction

The study uncovers that the pandemic information moderates the relationship between over-under reaction and investment decisions, suggesting that investors adjust their reactions based on evolving pandemic information, leading to more informed and rational investment choices (Jiang et al. 2022 ).

Herd behavior

It finds that COVID-19 pandemic information significantly reduces herd behavior among investors, encouraging them to make rational decisions rather than blindly following the majority (Nguyen et al. 2023 ).

In conclusion, this study illustrates that the COVID-19 pandemic has significantly moderated the relationship between behavioral biases and investment decisions. Furthermore, clustering analyses and agent-based outcomes suggest that younger, less experienced agents prone to herding behavior exhibit a higher propensity for such behavior and demonstrate lower performance in agent-based models. These findings pave the way for further research into additional cognitive biases and socio-demographic variables’ effects on investment decisions.

Implications

This study contributes to the field of behavioral finance that COVID-19 pandemic information sharing significantly moderates the relationship between behavioral biases (e.g., investors’ sentiments, overconfidence, over/under reaction, and herd behavior) and investment decisions. Therefore, policy implications stem from findings are substantial, and thus addressing behavioral biases during COVID-19 pandemic to mitigate the market inefficiencies and promote better decision-making. First, this study suggests that investing in comprehensive financial education plans will enhance the financial literacy of investors and enable them to better recognize the behavioral biases during times of uncertainty and crises. Second, findings imply that accurate and transparent information sharing about COVID-19 pandemic can better mitigate the behavioral biases, especially government interventions (e.g., National Command and Coordination Centre) ensuring reliable information can lead the investors to make more rational and informed investment decisions during the time of uncertainty and crises. Last, findings provide insights to policy makers that pandemic news and developments significantly influenced behavioral biases of investment decisions (Khurshid et al. 2021 ). For example, news about number of causalities, infection rates, vaccine progress, government stimulus packages, or stock market downturns had immediate effects on behavioral biases especially when an investor is overconfidence, over/under reaction, and herd behavior. In this sense, enhancing information transparency about COVID-19 news in media can reduce the influence of sensationalized news on investor decisions.

Limitations and call for future research

This study significantly enhances the understanding of behavioral factors’ impact on investors’ decision-making processes, presenting important findings within the context of the COVID-19 pandemic. While these contributions are notable, the research is subject to certain limitations that pave the way for future exploration and deeper investigation into this complex field.

Firstly, the study underscores the necessity for further research to validate its results through larger sample sizes and a more diverse array of respondents. Adopting a longitudinal design could prove particularly insightful, enabling an analysis of behavioral biases across different stages of the pandemic and providing a dynamic perspective on how investor behaviors evolve over time.

In addition, there’s a highlighted opportunity for future studies to delve into the behaviors influencing institutional investor decisions within Pakistan. The complex decision-making processes and investment portfolios of institutional investors, coupled with challenges like data availability and the heterogeneity among institutions, present a fertile ground for investigation. Such research could unravel how various factors, including market conditions and macroeconomic assessments, impact institutional investment strategies.

The study also points out the need to broaden the investigation to include other potential behavioral factors beyond those focused on in the current research, such as loss aversion, personality traits, anchoring, and recency biases. Expanding the scope of behavioral factors examined could significantly enrich the behavioral finance field by offering a more comprehensive view of the influences on investment decisions.

Moreover, while the insights gained from a Pakistani context during the COVID-19 pandemic are invaluable, extending the research to include global (e.g., China, Japan, USA) and other emerging markets (e.g., BRICS) would enhance understanding of the universality or specificity of behavioral biases in investment decisions across various economic, cultural, and regulatory environments.

Lastly, the study’s reliance on quantitative data points to the potential benefits of incorporating qualitative data into future research. Undertaking case studies within specific securities brokerages or investment banks could provide an in-depth investigation of investor behavior, generating new insights that could inspire further research.

To support the development of more sophisticated agent-based models and to foster collaborative research efforts, the study makes its source code available to other researchers. This openness to collaboration promises to stimulate innovative approaches to understanding and modeling investor behavior across diverse contexts, contributing to the advancement of the behavioral finance field.

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Wasim ul Rehman

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Omur Saltik

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Faryal Jalil

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Ethical approval

The data was collated through an online survey approach (questionnaire) during the last variant of COVID-19 where anonymity of the respondents is meticulously preserved. The respondents were not asked to provide their names, identification, address, or any other identifying elements. The authors minutely observed the ethical guidelines of the Declaration of Helsinki. In addition, we hereby certify that this study was conducted under the ethical approval guidelines of Office of Research Innovation and Commercialization, University of the Punjab granted under the office order No. D/ 409/ORIC dated 31-12-2021.

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The consent of participants was obtained through consent form during the last variant of COVID-19. The consent form contains the title of study, intent of study, procedure to participate, confidentiality, voluntary participation of respondents, questions/query and consent of the respondents. The respondents were requested to provide their willingness to participate in survey on consent form via email before filling the online-surveyed (questionnaire). Further, participants were also assured that their anonymity would be maintained and that no personal information or identifying element would be disclosed. The consent form is in the supplementary files.

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Rehman, W.u., Saltik, O., Jalil, F. et al. Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices. Humanit Soc Sci Commun 11 , 524 (2024). https://doi.org/10.1057/s41599-024-03011-7

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Does constructivism learning approach lead to developing creative thinking skills? The mediating role of online collaborative learning environments

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• S. Vijayakumar Bharathi   ORCID: orcid.org/0000-0002-9667-6181 1 &
• Mandaar B. Pande 1  

In this study, we evaluate the impact of online collaborative learning environments (OCLE) on the development of creative learning skills through a constructivism learning approach. OCLE is an online platform, which provides a convenient environment for students to engage, communicate, organize, collaborate, and retain their learning experiences. For this study, we have specifically chosen 6 out of the 12 tenets from the taxonomy of the constructivist tenets that were laid down in our earlier work. We have evaluated the intervention of OCLE between constructivism learning approach and the development of creative learning skills. Secondly, we have explored the role of OCLE in either enabling or inhibiting the development of creative thinking skills. The empirical study is conducted on a sample of 417 students pursuing their postgraduate (MBA) in management. The study finds that OCLE significantly mediates the impact of certain constructivist tenets such as optimizing known knowledge, experiential learning, and adaptive cognition towards developing creative thinking skills. In addition, OCLE significantly impacts towards developing creative thinking skills. Our study contributes in extending students’ online learning experience to a hybrid learning mode and the development of creative thinking skills in the post-pandemic environment.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/890/1/012163

Article   Google Scholar  

Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53 , 102118. https://doi.org/10.1016/j.ijinfomgt.2020.102118

Allen, M. (2008). Promoting critical thinking skills in online information literacy instruction using a constructivist approach. College & Undergraduate Libraries, 15 (1–2), 21–38. https://doi.org/10.1080/10691310802176780

Allen, S. J., Rosch, D. M., & Riggio, R. E. (2021). Advancing leadership education and development: Integrating adult learning theory. Journal of Management Education, 46 (2), 252–283. https://doi.org/10.1177/10525629211008645

Alt, D., & Raichel, N. (2020). Enhancing perceived digital literacy skills and creative self-concept through gamified learning environments: Insights from a longitudinal study. International Journal of Educational Research, 101 , 101561. https://doi.org/10.1016/j.ijer.2020.101561

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103 (3), 411. https://doi.org/10.1037/0033-2909.103.3.411

Babin, B. J., Hair, J. F., & Boles, J. S. (2008). Publishing research in marketing journals using structural equation modeling. Journal of Marketing Theory and Practice, 16 (4), 279–286. https://doi.org/10.2753/MTP1069-6679160401

Ballin, L., Balandin, S., Stancliffe, R. J., & Togher, L. (2012). The views of people who use speech generating devices on mentoring new learners. Disability and Rehabilitation: Assistive Technology, 7 (1), 63–74. https://doi.org/10.3109/17483107.2011.573438

Bengoa, D. S., Ganassali, S., Kaufmann, H. R., Rajala, A., Trevisan, I., van Berkel, J., Zulauf, K., & Wagner, R. (2018). Shared experiences and awareness from learning in a student multicultural environment: Measuring skills’ development in intercultural intensive programs. Journal of International Education in Business, 11 (1), 27–42. https://doi.org/10.1108/JIEB-01-2017-0006

Brandao, E., Adelfio, M., Hagy, S., & Thuvander, L. (2021). Collaborative pedagogy for co-creation and community outreach: An experience from architectural education in social inclusion using the miro tool BT. In D. Raposo, N. Martins, & D. Brandão (Eds.), Advances in human dynamics for the development of contemporary societies (pp. 118–126). Springer International Publishing.

Chapter   Google Scholar  

Brudzinski, M., Hubenthal, M., Fasola, S., & Schnorr, E. (2021). Learning in a crisis: Online skill building workshop addresses immediate pandemic needs and offers possibilities for future trainings. Seismological Research Letters, 92 (5), 3215–3230. https://doi.org/10.1785/0220200472

Bruggeman, B., Tondeur, J., Struyven, K., Pynoo, B., Garone, A., & Vanslambrouck, S. (2021). Experts speaking: Crucial teacher attributes for implementing blended learning in higher education. The Internet and Higher Education, 48 , 100772. https://doi.org/10.1016/j.iheduc.2020.100772

Burke, L. A., & Williams, J. M. (2008). Developing young thinkers: An intervention aimed to enhance children’s thinking skills. Thinking Skills and Creativity, 3 (2), 104–124. https://doi.org/10.1016/j.tsc.2008.01.001

Chamberlain, L., Lacina, J., Bintz, W. P., Jimerson, J. B., Payne, K., & Zingale, R. (2020). Literacy in lockdown: Learning and teaching during COVID-19 school closures. The Reading Teacher, 74 (3), 243–253. https://doi.org/10.1002/trtr.1961

Chen, B., & Hong, H. Y. (2016). Schools as knowledge-building organizations: Thirty years of design research. Educational Psychologist, 51 (2), 266–288. https://doi.org/10.1080/00461520.2016.1175306

Chisita, C. T., & Tsabedze, V. W. (2021). Massive open online courses (MOOCs): A tool for intercontinental collaboration in archives and records management education in Eswatini. Records Management Journal . https://doi.org/10.1108/RMJ-08-2020-0028

Cho, M. H., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34 (3), 290–301. https://doi.org/10.1080/01587919.2013.835770

Park, C., & Kim, D.-G. (2020). Perception of instructor presence and its effects on learning experience in online classes. Journal of Information Technology Education: Research, 19 , 475–488.

Google Scholar  

Clark, K. R. (2018). Learning theories: Constructivism. Radiologic Technology, 90 (2), 180–182.

Cronje, J. C. (2020). Towards a new definition of blended learning. Electronic Journal of e-Learning, 18 (2), 114–121.

Çuhadar, C. (2012). Exploration of problematic Internet use and social interaction anxiety among Turkish pre-service teachers. Computers & Education, 59 (2), 173–181. https://doi.org/10.1016/j.compedu.2011.12.029

de Acedo Lizarraga, M. L. S., de Acedo Baquedano, M. T. S., Mangado, T. G., & Cardelle-Elawar, M. (2009). Enhancement of thinking skills: Effects of two intervention methods. Thinking Skills and Creativity, 4 (1), 30–43. https://doi.org/10.1016/j.tsc.2008.12.001

Deng, L., Shen, Y. W., & Chan, J. W. W. (2021). Supporting cross-cultural pedagogy with online tools: Pedagogical design and student perceptions. TechTrends, 65 (5), 760–770. https://doi.org/10.1007/s11528-021-00633-5

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management . https://doi.org/10.1016/j.ijinfomgt.2020.102211

Elshami, W., Taha, M. H., Abuzaid, M., Saravanan, C., Al Kawas, S., & Abdalla, M. E. (2021). Satisfaction with online learning in the new normal: Perspective of students and faculty at medical and health sciences colleges. Medical Education Online, 26 (1), 1920090.

Fernandez, R., Rosenman, E. D., Plaza-Verduin, M., & Grand, J. A. (2022). Developing adaptive performance: A conceptual model to guide simulation-based training design. AEM Education and Training, 6 (3), e10762. https://doi.org/10.1002/aet2.10762

Feyzi Behnagh, R., & Yasrebi, S. (2020). An examination of constructivist educational technologies: Key affordances and conditions. British Journal of Educational Technology, 51 (6), 1907–1919. https://doi.org/10.1111/bjet.13036

Fromm, J., Radianti, J., Wehking, C., Stieglitz, S., Majchrzak, T. A., & vom Brocke, J. (2021). More than experience? On the unique opportunities of virtual reality to afford a holistic experiential learning cycle. The Internet and Higher Education, 50 , 100804. https://doi.org/10.1016/j.iheduc.2021.100804

Galustyan, O. V., Borovikova, Y. V., Polivaeva, N. P., Bakhtiyor, K. R., & Zhirkova, G. P. (2019). E-learning within the field of andragogy. International Journal of Emerging Technologies in Learning (iJET), 14 (09), 148–156. https://doi.org/10.3991/ijet.v14i09.10020

Ghazal, S., Al-Samarraie, H., & Wright, B. (2019). A conceptualization of factors affecting collaborative knowledge building in online environments. Online Information Review, 44 (1), 62–89. https://doi.org/10.1108/OIR-02-2019-0046

Gillham, B. (2008). Developing a questionnaire (2nd ed.). Continuum International Publishing Group.

Gordon, S. J. G., Bolwell, C. F., Raney, J. L., & Zepke, N. (2022). Transforming a didactic lecture into a student-centered active learning exercise—teaching equine diarrhea to fourth-year veterinary students. Education Sciences . https://doi.org/10.3390/educsci12020068

Groeneveld, W., Luyten, L., Vennekens, J., & Aerts, K. (2021). Exploring the role of creativity in software engineering. In 2021 IEEE/ACM 43rd international conference on software engineering: Software engineering in society (ICSE-SEIS) (pp. 1–9). https://doi.org/10.1109/ICSE-SEIS52602.2021.00009

Grover, S., Pandya, M., Ranasinghe, C., Ramji, S. P., Bola, H., & Raj, S. (2022). Assessing the utility of virtual OSCE sessions as an educational tool: A national pilot study. BMC Medical Education, 22 (1), 178. https://doi.org/10.1186/s12909-022-03248-3

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46 (1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017a). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117 (3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40 (3), 414–433. https://doi.org/10.1007/s11747-011-0261-6

Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017b). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117 (3), 442–458.

Hair, J. F., Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26 (2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Hair, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review . https://doi.org/10.1108/EBR-09-2015-0094

Haynie, M., & Shepherd, D. A. (2009). A measure of adaptive cognition for entrepreneurship research. Entrepreneurship Theory and Practice, 33 (3), 695–714. https://doi.org/10.1108/EBR-09-2015-0094

He, K. (2022). Supportive integration of information technology and subject teaching: Neo-constructivism. Innovative education informatization with Chinese characteristics (pp. 67–104). Singapore: Springer.

He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29 (1), 90–102. https://doi.org/10.1016/j.chb.2012.07.020

Henseler, J. (2012). PLS-MGA: A non-parametric approach to partial least squares-based multi-group analysis. In Challenges at the interface of data analysis, computer science, and optimization: Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21–23, 2010 (pp. 495–501). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_50

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing . Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014

Book   Google Scholar  

Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in advertising research: Basic concepts and recent issues . Edward Elgar Publishing. https://doi.org/10.1108/S1474-7979(2009)0000020014

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17 (2), 182–209.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43 , 115–135. https://doi.org/10.1007/s11747-014-0403-8

Ho, I. M. K., Cheong, K. Y., & Weldon, A. (2021). Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLoS ONE, 16 (4), e0249423. https://doi.org/10.1371/journal.pone.0249423

Höck, M., & Ringle, C. M. (2006, September). Strategic networks in the software industry: An empirical analysis of the value continuum. In IFSAM VIIIth world congress (Vol. 28, No. 2010). https://doi.org/10.1504/IJKMS.2010.030789

Horvat, N., Martinec, T., Lukačević, F., Perišić, M. M., & Škec, S. (2022). The potential of immersive virtual reality for representations in design education. Virtual Reality . https://doi.org/10.1007/s10055-022-00630-w

Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3 (4), 424–453.

Huang, W., Walkington, C., & Nathan, M. J. (2023). Coordinating modalities of mathematical collaboration in shared VR environments. International Journal of Computer-Supported Collaborative Learning . https://doi.org/10.1007/s11412-023-09397-x

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20 (2), 195–204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2%3c195::AID-SMJ13%3e3.0.CO;2-7

Ismail, H., Khelifi, A., & Harous, S. (2022). A cognitive style based framework for usability evaluation of online lecturing platforms—A case study on zoom and teams. International Journal of Engineering Pedagogy (iJEP), 12 (1), 104–122. https://doi.org/10.3991/ijep.v12i1.25295

Kanakana-Katumba, M. G., & Maladzhi, R. (2019). Online learning approaches for science, engineering and technology in distance education. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2019 , 930–934. https://doi.org/10.1109/IEEM44572.2019.8978892

Kaufman, D. (2004). 14. Constructivist issues in language learning and teaching. Annual Review of Applied Linguistics, 24 , 303–319. https://doi.org/10.1017/S0267190504000121

Ke, F. (2010). Examining online teaching, cognitive, and social presence for adult students. Computers & Education, 55 (2), 808–820. https://doi.org/10.1016/j.compedu.2010.03.013

Kesler, A., Shamir-Inbal, T., & Blau, I. (2021). Active learning by visual programming: Pedagogical perspectives of instructivist and constructivist code teachers and their implications on actual teaching strategies and students’ programming artifacts. Journal of Educational Computing Research, 60 (1), 28–55. https://doi.org/10.1177/07356331211017793

Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4 (2), 193–212. https://doi.org/10.5465/amle.2005.17268566

Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development . FT Press.

Kripfganz, S., & Schneider, D. C. (2020). Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models 1. Oxford Bulletin of Economics and Statistics, 82 (6), 1456–1481. https://doi.org/10.1111/obes.12377

Kuge, N., & Zhanikeev, M. (2022). Educational content delivery in mixed online/offline university campuses BT. In K. Arai (Ed.), Proceedings of the future technologies conference (FTC) 2021 (Vol. 3, pp. 702–711). Springer International Publishing

Lei, M., & Medwell, J. (2021). Impact of the COVID-19 pandemic on student teachers: How the shift to online collaborative learning affects student teachers’ learning and future teaching in a Chinese context. Asia Pacific Education Review, 22 (2), 169–179. https://doi.org/10.1007/s12564-021-09686-w

Lending, D., May, J., Ezell, J. D., & Dillon, T. (2022). Discovering effective requirements elicitation techniques using a multivocal ethnographic framework. International Journal of Innovation and Learning, 31 (2), 236–263. https://doi.org/10.1504/IJIL.2022.120649

Li, Y., Liu, C., Xu, K., Hao, X., & Sui, S. (2022). A seven-question based critical thinking framework for cultivating innovation talents in engineering research and its implementation perspectives. Proceedings of the Institution of Mechanical Engineers, Part b: Journal of Engineering Manufacture . https://doi.org/10.1177/09544054221076232

Liguori, E. W., Winkler, C., Zane, L. J., Muldoon, J., & Winkel, D. (2021). COVID-19 and necessity-based online entrepreneurship education at US community colleges. Journal of Small Business and Enterprise Development, 28 (6), 821–830. https://doi.org/10.1108/JSBED-09-2020-0340

Liu, J., & Gao, Y. (2022). Higher education internationalisation at the crossroads: Effects of the coronavirus pandemic. Tertiary Education and Management, 28 (1), 1–15. https://doi.org/10.1007/s11233-021-09082-4

Liu, X., & Zhang, L. (2024). Exploring the relationship between teachers’ professional capital and technology-enhanced teaching innovation: The mediating role of constructivist belief. Teaching and Teacher Education, 139 , 104434. https://doi.org/10.1016/j.tate.2023.104434

Liu, Z., Sampaio, P., Pishchulov, G., Mehandjiev, N., Cisneros-Cabrera, S., Schirrmann, A., Jiru, F., & Bnouhanna, N. (2022). The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration. Computers in Industry . https://doi.org/10.1016/j.compind.2022.103623

Brewer, J., & Hunter, A. (2006). Foundations of multimethod research: Synthesizing styles . Sage.

Maloney, S., Tai, J. H. M., Paynter, S., Lo, K., & Ilic, D. (2013). Self-directed online learning modules: Students’ behaviours and experiences. Pharmacy, 1 (1), 8–15. https://doi.org/10.3390/pharmacy1010008

Matthews, D., Biney, H., & Abbot-Smith, K. (2018). Individual differences in children’s pragmatic ability: A review of associations with formal language, social cognition, and executive functions. Language Learning and Development, 14 (3), 186–223. https://doi.org/10.1080/15475441.2018.1455584

Merritt, E. G., Stern, M. J., Powell, R. B., & Frensley, B. T. (2022). A systematic literature review to identify evidence-based principles to improve online environmental education. Environmental Education Research, 28 (5), 674–694. https://doi.org/10.1080/13504622.2022.2032610

Morris, T. H. (2020). Experiential learning—A systematic review and revision of Kolb’s model. Interactive Learning Environments, 28 (8), 1064–1077. https://doi.org/10.1080/10494820.2019.1570279

Moseikina, M., Toktamysov, S., & Danshina, S. (2022). Modern technologies and gamification in historical education. Simulation & Gaming, 53 (2), 135–156. https://doi.org/10.1177/10468781221075965

Moster, M., Ford, D., & Rodeghero, P. (2021). “Is My Mic On?” Preparing SE students for collaborative remote work and hybrid team communication. In 2021 IEEE/ACM 43rd international conference on software engineering: Software engineering education and training (ICSE-SEET) (pp. 89–94). https://doi.org/10.1109/ICSE-SEET52601.2021.00018

Mukhalalati, B. A., & Taylor, A. (2019). Adult learning theories in context. Journal of Medical Education and Curricular Development . https://doi.org/10.1177/2382120519840332

Nikimaleki, M., & Rahimi, M. (2022). Effects of a collaborative AR-enhanced learning environment on learning gains and technology implementation beliefs: Evidence from a graduate teacher training course. Journal of Computer Assisted Learning . https://doi.org/10.1111/jcal.12646

Njenga, J. K. (2018). Sociocultural paradoxes and issues in e-learning use in higher education Africa. Globalisation, Societies and Education, 16 (1), 120–133. https://doi.org/10.1080/14767724.2017.1390664

Novak, E., & Mulvey, B. K. (2020). Enhancing design thinking in instructional technology students. Journal of Computer Assisted Learning . https://doi.org/10.1111/jcal.12470

O’Connor, K. (2022). Constructivism, curriculum and the knowledge question: Tensions and challenges for higher education. Studies in Higher Education, 47 (2), 412–422. https://doi.org/10.1080/03075079.2020.1750585

Ozkaya, I. (2021). The future of software engineering work. IEEE Software, 38 (05), 3–6. https://doi.ieeecomputersociety.org/10.1109/MS.2021.3089729

Paepcke-Hjeltness, V. (2021). Rapid Idea development, translating face-to-face interactions to virtual platforms BT. In C. S. Shin, G. Di Bucchianico, S. Fukuda, Y.-G. Ghim, G. Montagna, & C. Carvalho (Eds.), Advances in industrial design (pp. 125–133). Springer International Publishing.

Pande, M., & Bharathi, S. V. (2020). Theoretical foundations of design thinking—A constructivism learning approach to design thinking. Thinking Skills and Creativity, 36 , 100637. https://doi.org/10.1016/j.tsc.2020.100637

Pichai, S. (2023, February 6). An important next step on our AI journey. Retrieved February 24, 2023 from https://bit.ly/3XS0M36

Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Cukurova, M., Bartindale, T., Chen, P., Marshall, H., Richardson, D., & Gasevic, D. (2022). The question-driven dashboard: How can we design analytics interfaces aligned to teachers’ inquiry? In LAK22: 12th international learning analytics and knowledge conference (pp. 175–185). https://doi.org/10.1145/3506860.3506885

Ramkissoon, P., Belle, L. J., & Bhurosy, T. (2020). Perceptions and experiences of students on the use of interactive online learning technologies in Mauritius. International Journal of Evaluation and Research in Education, 9 (4), 833–839.

Ramli, N. A., Latan, H., & Nartea, G. V. (2018). Why should PLS-SEM be used rather than regression? Evidence from the capital structure perspective. In Partial least squares structural equation modeling: Recent advances in banking and finance (pp. 171–209) https://doi.org/10.1007/978-3-319-71691-6_6

Reimers, F. M., & Marmolejo, F. (2022). Leading learning during a time of crisis: Higher education responses to the global pandemic of 2020. University and school collaborations during a pandemic (pp. 1–41). Cham: Springer.

Reuschl, A. J., Deist, M. K., & Maalaoui, A. (2022). Digital transformation during a pandemic: Stretching the organizational elasticity. Journal of Business Research, 144 , 1320–1332. https://doi.org/10.1016/j.jbusres.2022.01.088

Richards, G. (2020). Tourism in challenging times: Resilience or creativity? Tourism Today, 2020 (19), 8–15. https://doi.org/10.1016/j.procs.2022.07.125

Rodenburg, D., Hungler, P., Etemad, S. A., Howes, D., Szulewski, A., & Mclellan, J. (2018). Dynamically adaptive simulation based on expertise and cognitive load. 2018 IEEE Games, Entertainment, Media Conference (GEM) (pp. 1–6). IEEE.

Rodrigues, J. J., Sabino, F. M., & Zhou, L. (2011). Enhancing e-learning experience with online social networks. IET Communications, 5 (8), 1147–1154. https://doi.org/10.1049/iet-com.2010.0409

Rigdon, E. E. (1998). The equal correlation baseline model for comparative fit assessment in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 5 (1), 63–77. https://doi.org/10.1080/10705519809540089

Saleeb, N. (2021). Closing the chasm between virtual and physical delivery for innovative learning spaces using learning analytics. The International Journal of Information and Learning Technology, 38 (2), 209–229. https://doi.org/10.1108/IJILT-05-2020-0086

Sarstedt, M., & Mooi, E. (2014). A concise guide to market research: The process, data, and methods using IBM SPSS statistics . Springer.

Sathanarugsawait, B., Samat, C., & Wattanachai, S. (2020). Survey results of learner context in the development of constructivist learning environment model to enhance creative thinking with massive open online course (MOOCS) for higher education BT. In T.-C. Huang, T.-T. Wu, J. Barroso, F. E. Sandnes, P. Martins, & Y.-M. Huang (Eds.), Innovative technologies and learning (pp. 465–474). Springer International Publishing.

Silva, H., Lopes, J., Morais, E., & Dominguez, C. (2021). Cooperative learning and critical thinking in face to face and online environments. In A. Reis, J. Barroso, J. B. Lopes, T. Mikropoulos, & C.-W. Fan (Eds.), Technology and innovation in learning, teaching and education (pp. 168–180). Springer International Publishing.

Sørensen, K., Van den Broucke, S., Pelikan, J. M., Fullam, J., Doyle, G., Slonska, Z., Osborne, H., & Brand, H. (2013). Measuring health literacy in populations: Illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health, 13 , 1–10.

Suebsom, K. (2020, January). The use of blended learning. In Proceedings of the 2020 the 3rd international conference on computers in management and business (pp. 201–206). New York: Association for Computing Machinery

Swan, K. (2005). A constructivist model for thinking about learning online. Elements of quality online education. Engaging Communities, 6 , 13–31.

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379 (6630), 313–313. https://doi.org/10.1126/science.adg7879

Turakhia, D., Ludgin, D., Mueller, S., & Desportes, K. (2023). Understanding the educators’ practices in makerspaces for the design of education tools. Educational Technology Research and Development . https://doi.org/10.1007/s11423-023-10305-1

Vallis, C., & Redmond, P. (2021). Introducing design thinking online to large business education courses for twenty-first century learning. Journal of University Teaching & Learning Practice, 18 (6), 213–234.

VanOostveen, R., Desjardins, F., & Bullock, S. (2019). Professional development learning environments (PDLEs) embedded in a collaborative online learning environment (COLE): Moving towards a new conception of online professional learning. Education and Information Technologies, 24 (2), 1863–1900. https://doi.org/10.1007/s10639-018-9686-6

Wannapiroon, N., & Pimdee, P. (2022). Thai undergraduate science, technology, engineering, arts, and math (STEAM) creative thinking and innovation skill development: A conceptual model using a digital virtual classroom learning environment. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10849-w

Wilm, M. C., & Gerleve, C. V. H. (2021). The link between entrepreneurial passion and tolerance for failure mediated by adaptive cognition. In Academy of management proceedings (Vol. 2021, No. 1, p. 11141). Briarcliff Manor, NY: Academy of Management

Winne, P. H. (2021). Open learner models working in symbiosis with self-regulating learners: A research agenda. International Journal of Artificial Intelligence in Education, 31 (3), 446–459. https://doi.org/10.1007/s40593-020-00212-4

Wu, M. (2022). Effects of feedback on individual creativity in social learning: an experimental study. Kybernetes . https://doi.org/10.1108/K-07-2021-0602

Xudong, Z., & Li, J. (2020). Investigating ‘collective individualism model of learning’: From Chinese context of classroom culture. Educational Philosophy and Theory, 52 (3), 270–283. https://doi.org/10.1080/00131857.2019.1638762

Zacharaki, E. I., Triantafyllidis, A., Carretón, R., Loeck, M., Michalellis, I., Michalakis, G., Chantziaras, G., Segkouli, S., Giakoumis, D., Moustakas, K., & others. (2022). A user evaluation study of augmented and virtual reality tools for training and knowledge transfer. In International conference on human-computer interaction (pp. 291–304).

Zhan, Q., Chen, X., & Retnawati, E. (2022). Exploring a construct model for university makerspaces beyond curriculum. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10761-3

Zhan, Z., Wei, Q., & Hong, J. C. (2021). Cellphone addiction during the Covid-19 outbreak: How online social anxiety and cyber danger belief mediate the influence of personality. Computers in Human Behavior, 121 , 106790. https://doi.org/10.1016/j.chb.2021.106790

Zheng, L. (2021). Promote collaborative knowledge building through teacher guidance. Data-driven design for computer-supported collaborative learning (pp. 103–114). Singapore: Springer.

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Vijayakumar Bharathi, S., Pande, M.B. Does constructivism learning approach lead to developing creative thinking skills? The mediating role of online collaborative learning environments . J. Comput. Educ. (2024). https://doi.org/10.1007/s40692-024-00321-2

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ORIGINAL RESEARCH article

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health.

• 1 School of Management, Guangzhou University, Guangzhou, China
• 2 Guangzhou Xinhua University, Dongguan, China
• 3 School of Public Administration, Guangzhou University, Guangzhou, China
• 4 School of Economics and Statistics, Guangzhou University, Guangzhou, China
• 5 School of Journalism and Communication, Guangzhou University, Guangzhou, China

Introduction: This paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.

Methods: To assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.

Results: Analysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises ( r = 0.523 ; p < 0.001 ), Moreover, the presence of innovative environmental protection technologies on these platforms was found to positively influence the enforcement of environmental policies, with a calculated impact ratio of ( a ∗ b / c = 55.31 % ). An intermediary analysis highlighted that environmental innovation technology plays a mediating role in this process. Additionally, adjustment analysis showed that enterprises of various sizes and industries respond differently to digital platforms, indicating the need for tailored environmental policies

Discussion: These findings underscore the pivotal role of digital platforms in enhancing CSR efforts and public health by fostering improved environmental practices among corporations. The mediating effect of environmental innovation technologies suggests that digital platforms not only facilitate direct environmental actions but also enhance the efficiency and effectiveness of such initiatives through technological advances. The variability in response by different enterprises points to the importance of customizable strategies in policy formulation. By offering empirical evidence of digital platforms’ potential to advance CSR and public health through environmental initiatives, this paper contributes to the ongoing dialogue on sustainable development goals. It provides practical insights for enterprises and policy implications for governments striving to craft more effective environmental policies and strategies.

1 Introduction

Global environmental issues have gained prominence in today’s society, raising a great deal of concern. Environmental challenges such as climate change, resource depletion and ecosystem destruction threaten the sustainable development of the earth and the survival of mankind ( 1 , 2 ). In this context, enterprises not only need to find a balance between economic interests and environmental protection, but also need to hypothesize social responsibilities and contribute to sustainable development ( 3 ). As a tool for information dissemination, cooperation and interaction, and resource integration, digital platform is regarded as an emerging force that may have a far-reaching impact on corporate environmental protection behavior and social responsibility ( 4 ). In the past decades, corporate social responsibility (CSR) has become an important part of business practice. Enterprises no longer only pay attention to economic performance, but link their economic activities with social and environmental issues to ensure sustainable development ( 5 – 7 ). Meanwhile, the rise of digital platform has changed the interaction between enterprises and their stakeholders, providing enterprises with more opportunities to disseminate environmental information, cooperate to solve environmental problems, and supervise their environmental protection behavior ( 8 ). However, despite these potential opportunities, there are still many unknown factors about the actual impact of digital platforms on corporate environmental behavior and social responsibility ( 9 ).

In recent years, with the rapid development of digital technology, digital platform has become an important force to promote social change. Especially in corporate social responsibility and public health, the role of digital platform has become increasingly prominent. Early studies such as Wang et al. ( 10 ) have pointed out that digital transformation can promote enterprises to implement environmental protection policies and social responsibility plans more efficiently. However, there is still a lack of existing literature on how the digital platform affects the sustainable practice of enterprises under the guidance of the development of environmental science, especially the contribution to public health. At present, digital platform plays a vital role in the practice of CSR. Through digital means, enterprises can manage resources more effectively, improve energy efficiency, reduce carbon emissions and other environmental protection behaviors. Taking an energy company as an example, the company uses digital platform to implement intelligent energy management system, monitor energy usage, and optimize energy distribution, thus reducing energy waste and improving energy utilization efficiency. Through digital monitoring and data analysis, enterprises can know the energy consumption in real time, adjust production plans in time to reduce carbon emissions, and realize green production. These measures not only help enterprises to comply with environmental laws and regulations and fulfill their social responsibilities, but also bring them economic benefits and brand reputation. Looking forward to the future, the potential of digital platform lies in promoting enterprises to achieve sustainable development goals and promoting environmental protection behavior and social responsibility practice to a higher level. The continuous innovation and application of digital technology will provide more environmental protection solutions and tools for enterprises and support the realization of environmentally friendly production. However, the digital platform also faces some challenges, such as data privacy protection and information security risks, which need to be effectively controlled. Meanwhile, in the process of digital transformation, enterprises may face challenges in technology upgrading and talent training, and it is necessary to strengthen their understanding and application ability of digital technology. Considering the development perspective of environmental science, the relationship between digital platform and CSR is very important. Through the application of digital platform, enterprises can better practice environmental protection behavior, promote sustainable development, and integrate social responsibility into all aspects of business operations. The in-depth discussion of this relationship fills the gap in the existing research and provides new ideas and viewpoints for the related influence in the field of public health. By combining the concepts of digital platform, environmental science and CSR, future research will help to better explore the potential role of digital platform in CSR and public health, and promote the development of enterprises in a more sustainable and socially responsible direction. Therefore, this paper attempts to fill this knowledge gap and explore the subject through empirical research. Specifically, this paper uses the methods of descriptive statistical analysis, correlation analysis and hypothesis test analysis to evaluate the relationship between the use of digital platforms and corporate environmental behavior, investigates the impact of digital platforms on CSR policies and practices, explores the intermediary variables and moderating variables between digital platforms and corporate environmental behavior, and compares the differences in the impact of digital platforms on corporate environmental behavior and social responsibility between different industries and geographical regions. This paper deeply discusses the important role of digital platform in enterprise operation and the possible positive impact of corporate social responsibility on public health and environmental protection. With the acceleration of digital transformation, enterprises increasingly rely on digital platforms to optimize their operational efficiency and market competitiveness, which provides new opportunities and challenges for enterprises to fulfill their social and environmental responsibilities. By revealing how the digital platform can help enterprises to better implement CSR strategy, and then have a positive impact on environmental protection, this paper aims to provide policy makers and business managers with empirical insights and suggestions to promote the realization of sustainable development goals.

In order to achieve the above research objectives, this paper adopts various research methods, including quantitative questionnaire survey, to collect relevant data of enterprises and digital platforms. Then, descriptive statistical analysis is used to summarize the basic characteristics of the data, correlation analysis is used to test the relationship between variables, and hypothesis testing analysis is used to verify the research hypothesis. In addition, intermediary analysis and adjustment analysis are used to deeply understand the influence mechanism of digital platform on corporate environmental behavior and social responsibility. This paper fills the knowledge gap of the influence of digital platform on corporate environmental behavior and social responsibility, and provides practical and policy enlightenment. By deeply understanding the relationship between digital platform and sustainable development of enterprises, it can provide strong support for enterprises and governments to formulate more effective environmental protection policies and strategies.

There are three innovations in this paper. First, from the perspective of environmental science development, the influence mechanism of digital platform on corporate environmental behavior and social responsibility is deeply explored. The second is to put forward the application strategy of digital platform in corporate environmental behavior and social responsibility to provide guidance for corporate practice. Thirdly, by means of questionnaire survey, descriptive statistical analysis, correlation analysis and hypothesis test analysis, the influence mechanism of digital platform on corporate environmental behavior and social responsibility is comprehensively studied.

2 Literature review

Scholars have carried out extensive research in the field of corporate environmental behavior and CSR. They paid attention to the motivation, influencing factors and effects of corporate environmental protection behavior, and discussed the influence of CSR on corporate performance and sustainable development from different dimensions. Afsar and Umrani ( 11 ) investigated the influence of perceived CSR on employees’ environmental behavior. The results showed that perceived CSR had a significant and positive impact on environmental commitment. Raza et al. ( 12 ) investigated hotel employees’ views on CSR activities and their influence on employees’ voluntary environmental protection behavior based on the theory of social exchange and identity. The results showed that CSR had a direct impact on employees’ voluntary environmental protection behavior. Latif et al. ( 13 ) analyzed the relationship between CSR and employees’ environmental behavior from the perspective of sustainable development, and found that employees’ perceived CSR actively promoted employees’ environmental behavior. Deng et al. ( 14 ) studied the relationship between CSR initiatives in hospitals and employees’ environmental behavior, and found that CSR directly and indirectly affected employees’ environmental behavior through environment-specific transformational leadership. Guan et al. ( 15 ) proposed that CSR was mainly related to the environmental performance and economic performance of enterprises. Nowadays, people can improve the environmental performance and economic performance of enterprises by promoting employees’ environmental behavior and altruistic values, and realize CSR. Giacalone et al. ( 16 ) believed that CSR involved the aim of having a positive impact on the community operated by the analyzed company. International organizations and government agencies had also issued a series of environmental science guidelines to encourage enterprises to adopt sustainable development practices, reduce carbon emissions and protect ecosystems. The Global Environment Outlook report provided a comprehensive assessment of the global environmental situation, and called on governments, enterprises and all sectors of society to take actions to reduce carbon emissions, protect ecosystems and promote sustainable development. The report included detailed analysis and suggestions on many environmental problems such as climate change, biodiversity loss and land degradation, and encourages enterprises to take environmental protection measures to promote the realization of global sustainable development goals. It shows that the environmental protection behavior of enterprises has a far-reaching impact on their economic performance and social reputation. Environmental protection behavior not only helps to reduce the environmental footprint of enterprises, but also improves the trust of consumers and investors in enterprises. However, the environmental protection behavior of enterprises is influenced by many factors, including laws and regulations, market pressure and social expectations. Therefore, it has become an important topic to study how to promote enterprises to participate in environmental protection activities more actively.

The emergence of digital platform provides a new perspective for studying corporate environmental behavior and CSR. Among them, technologies and algorithms play a key role in the digital platform, which can be used for data analysis, user behavior prediction and information dissemination. The participation of artificial intelligence (AI) can effectively interact with experts and non-experts in different social places to promote the wise judgment of opaque artificial intelligence systems and realize their democratic governance ( 17 ). Li ( 18 ) believed that big data analysis played an important role in green governance and CSR. Kong and Liu ( 19 ) thought that digital transformation has greatly promoted CSR, and it was helpful to improve pollution control ability and internal control efficiency in enterprises with low financing constraints and high regulatory pressure, thus improving CSR performance. Li ( 20 ) evaluated the financial investment environment of enterprises based on blockchain and cloud computing, and found that cloud computing technology and blockchain technology expanded the construction performance of financial investment data from 5.98 to 9.27. The computing performance was improved by 3.29. Based on two-stage structural equation modeling-artificial neural network (ANN) method, Najmi et al. ( 21 ) discussed the role of consumers in the recycling plan of scrapped mobile phones. Yan et al. ( 22 ) used two-stage structural equation modeling and ANN to analyze the impact of the adoption of financial technology on the sustainable development performance of banking institutions. The research results showed that green finance and green innovation fully mediate the relationship between the application of financial technology and the sustainable development performance of banking institutions ( 22 ). Diaz and Nguyen ( 23 ) predicted the minimum prediction error of CSR index through gray correlation analysis and gray correlation analysis, and found that BPN model had the smallest prediction error, which was better than recurrent neural network (RNN) and radial basis function neural network model. Ezzi et al. ( 24 ) analyzed the important role of blockchain technology in explaining CSR performance, and the results showed that the implementation of blockchain technology had a significant and positive impact on CSR performance.

Wang et al. ( 25 ) constructed a recommendation and resource optimization model by using neural network algorithm from the perspective of cultural and creative industries to promote enterprise project decision-making and resource optimization. The research showed that the entrepreneurial project recommendation and resource optimization model can significantly improve the recognition accuracy, reduce the prediction error, and contribute to the sustainable development of social economy and the optimization of entrepreneurial resources. Combined with the research content of this paper, these research results can provide effective decision-making reference for enterprises and promote the realization of sustainable development goals. Wang et al. ( 26 ) used blockchain technology to build an intelligent contract, established a risk management system for online public opinion, and tracked public opinion through risk correlation tree technology, thus improving the accuracy of risk prediction and credibility detection. The research results showed that with the support of blockchain technology, the three experimental schemes designed can reasonably predict the risk and detect the credibility of NPO. This work was helpful to optimize the control measures of network environment and provide an important reference for improving the management level of network public opinion. Deng et al. ( 27 ) promoted the mechanism of public participation and enhanced the vitality of the economic market of resource-based cities by increasing policy intervention. This study had important reference value for promoting urban resource management and economic efficiency. Li et al. ( 28 ) paid attention to the influence of the pilot policy of low-carbon cities on urban entrepreneurial activities and its role in promoting green development. The results showed that the pilot policy of low-carbon cities generally inhibits entrepreneurial activities, but the level of green innovation can alleviate this inhibitory effect. In addition, the pilot policy of low-carbon cities inhibited the entrepreneurial activities of high-carbon industries, while encouraging the entrepreneurial activities of emerging industries, which led to the changes and upgrading of industrial structure. Li et al. ( 29 ) discussed the development path of clean energy and related issues of sustainable development of mining projects in the ecological environment driven by big data. Through this study, it was hoped to provide empirical support and decision-making reference for mining projects in the development of clean energy, promote the sustainable development of mining industry and realize a win-win situation of economic and ecological benefits. This was of great significance for protecting the ecological environment and realizing the sustainable utilization of resources. Li et al. ( 30 ) investigated the influence of regional digital finance development on corporate financing constraints. It was found that digital finance can significantly alleviate the financing constraints of enterprises, and the impact on small and medium-sized enterprises and private enterprises was more significant. Li et al. ( 31 ) discussed the impact of climate change on corporate environmental, social and governance performance. According to the empirical results, the environmental, social, and governance (ESG) performance of enterprises was significantly inhibited by climate change. It was also found that eliminating the mismatch between internal and external resources would help to alleviate the adverse impact of climate change on ESG performance.

The above literature review provides a comprehensive overview of the relevant research status and scholars’ views on corporate environmental behavior, CSR and digital platform. The research shows that scholars have carried out extensive research in the fields of corporate environmental behavior and CSR, and paid attention to different aspects of these fields, including environmental commitment, environmental behavior of employees, and sustainable development performance. Their research reveals the profound influence of environmental protection behavior of enterprises on their economic performance and social reputation, and the direct influence of CSR on employees’ voluntary environmental protection behavior. In addition, as a new technology and tool, digital platform has attracted the interest of research circles. Technologies and algorithms play a key role in the digital platform, which can be used for data analysis, user behavior prediction and information dissemination, thus affecting the environmental protection behavior and CSR of enterprises. Many studies have shown that AI, big data analysis, blockchain and other technologies have a positive impact on CSR performance and environmental protection behavior ( 32 – 35 ). However, these studies also have some limitations, such as differences in research methods, limitations in sample selection and heterogeneity between different fields. Therefore, this paper aims to explore the influence mechanism of digital platform on corporate environmental behavior and social responsibility, adopt various research methods, and pay attention to the differences between different industries and geographical regions. This will help to fill the knowledge gap in existing research and provide more specific guidance for enterprises and policy makers to promote the realization of sustainable development goals.

The design of this paper focuses on the interaction between digital platform and corporate social responsibility and its influence on environmental protection behavior, which reflects the complexity and scientific value of the study. Based on the theoretical framework and previous empirical research, this paper investigates how the digital platform affects the environmental protection behavior by promoting the practice of corporate social responsibility. This not only deepens the understanding of the role of digital platform in the field of corporate social responsibility, but also provides a new perspective on how to use digital technology to promote environmentally friendly behavior of enterprises, thus filling the gaps in the existing literature.

3 Research methodology

3.1 cross-influence of csr and development of environmental science.

CSR and environmental science development are two interrelated fields, and their cross-influence is very important for understanding the mechanism behind corporate environmental protection behavior. This section deeply discusses the relationship between CSR and the development of environmental science, and establish the theoretical basis of the research. In this section, the guiding principles of environmental science development, including environmental protection and sustainable development policy documents issued by international organizations such as the United Nations Environment Programme and government agencies, are shown in Table 1 .

Table 1 . Guidance document for the development of environmental science.

In Table 1 , the common goal of core policies and plans is to encourage enterprises to adopt sustainable development practices, reduce carbon emissions and protect ecosystems, thus promoting global sustainable development. Enterprises can fulfill their social and environmental responsibilities by actively participating in these initiatives and complying with relevant policies. Meanwhile, they can gain economic and reputation benefits in terms of sustainability. These policies and plans provide a framework and guidance for enterprises to play an active role in environmental protection behavior ( 36 , 37 ).

CSR covers the social and environmental impacts of enterprises in their business activities, and emphasizes the active obligations of enterprises in fulfilling their social responsibilities ( 38 ). Figure 1 shows the cross influence of CSR and the development of environmental science.

Figure 1 . Cross-influence of CSR and the development of environmental science.

In Figure 1 , in the cross-influence between CSR and environmental science, the core principles and active obligations of CSR play a key role. The core principles of CSR, such as social responsibility, transparency and sustainability, guide enterprises to actively consider social and environmental factors in their business activities. Meanwhile, CSR, as an active obligation, requires enterprises not only to fulfill their legal obligations, but also to actively participate in solving social and environmental problems. These behaviors are the concrete application of CSR in the environmental field, which shows how enterprises actively fulfill their social and environmental responsibilities and promote the practice of sustainable development.

3.2 Potential mechanism of digital platform in enterprise environmental protection behavior

Digital platform refers to a platform based on digital technology and Internet, which connects different participants and provides various services and solutions through online interaction and data sharing ( 39 ). Figure 2 shows the technical architecture of digital platform.

Figure 2 . Digital platform technology architecture.

The technical architecture of Figure 2 digital platform includes infrastructure, middleware, service layer, data layer, application layer and user interface. The user interface is the part where users interact with the digital platform, which provides the functions of users to operate and manage the platform. The application layer is responsible for handling business logic and functions. The data layer is responsible for data storage, reading, updating and deleting, and provides data access interfaces for the application layer. The service layer is a part that provides various services, and provides a series of interfaces and functions for the application layer to call and use. Middleware is a part that connects various components and levels, provides a mechanism for data exchange and communication, and ensures the coordination and interaction between various parts. Infrastructure provides computing resources and storage space to ensure the stability and reliability of the digital platform ( 40 , 41 ). Figure 3 shows the potential mechanism of digital platform in enterprise environmental protection behavior.

Figure 3 . Potential mechanism of digital platform in enterprise environmental protection behavior.

In Figure 3 , digital platform plays an important role in corporate environmental behavior. Through data analysis, resource management, carbon management, environmental protection innovation and other mechanisms, the digital platform helps enterprises to better fulfill their social and environmental responsibilities, promote the practice of sustainable development, improve the environmental performance of enterprises, create economic value for enterprises, and promote the positive relationship between environmental protection and sustainability ( 42 ).

3.3 Research hypothesis

The research hypothesis is a speculative statement about the relationship among different variables. The research hypothesis of this paper focus on the influence of digital platform on corporate environmental behavior and social responsibility.

Hypothesis 1 : There is a positive correlation between the widespread application of digital platforms and corporate environmental protection behavior.

Hypothesis 2 : There is a positive correlation between the environmental protection innovation technology of digital platform and the implementation of environmental protection policies of enterprises.

Hypothesis 3 : There is a positive correlation between social responsibility tools of digital platform and CSR activities.

Hypothesis 4 : There is a positive correlation between enterprise scale and industry type on digital platform and enterprise environmental behavior.

3.4 Method of data capture

In this study, the questionnaire design is to explore the influence of digital platform on corporate social responsibility practice by investigating employees in private enterprises. In order to ensure that the questionnaire can accurately reflect the actual digital actions and CSR activities of enterprises, a series of measures have been taken to enhance the reliability and validity of the questionnaire. Firstly, before designing the problem, the relationship between CSR and the development of environmental science is deeply studied, and the cross influence of CSR and environmental science is clarified. With reference to the policy documents on environmental protection and sustainable development issued by international organizations such as the United Nations Environment Programme, the theoretical basis of the research is constructed. This helps to ensure that the questionnaire design is closely related to the research objectives. Secondly, in the process of questionnaire design, 20 professionals with relevant backgrounds are invited to fill in the first edition of the questionnaire, and the expression and order of questions are adjusted according to their feedback to improve the clarity and logic of the questionnaire. This step is helpful to optimize the questionnaire design, ensure that the questions are accurate and clear, and capture the required information effectively. In addition, referring to the published related research, a measurement tool is constructed based on the indicators used in these studies to ensure the relevance and effectiveness of the questionnaire. In order to further improve the reliability and representativeness of the questionnaire, the online survey platform is used to distribute the questionnaire, and a reminder mechanism is set up to increase the response rate. Meanwhile, small rewards are provided for participants who completed the questionnaire to ensure the data quality. Cronbach’s α coefficient and exploratory factor analysis are used to verify the internal consistency test of sample data to evaluate the consistency and reliability of the questionnaire results. In addition, Pearson correlation coefficient is used to evaluate the correlation among different variables to ensure the accuracy and reliability of data analysis. In the questionnaire design, the respondents of private enterprises are divided into three categories: managers, team members and ordinary employees to ensure that employees with different positions and responsibilities are covered to fully understand the digital actions and CSR activities of enterprises. Through the questionnaire collection and analysis of employees in different positions, people can better understand the views and practices of digital platforms and environmental protection behaviors at all levels within the enterprise, and thus draw more objective research conclusions. The comprehensive application of the above measures makes it possible to explore the influence of digital platform on corporate social responsibility practice more comprehensively and accurately, and ensure that the obtained data has high credibility and representativeness, thus providing a solid foundation for subsequent analysis and conclusions. The specific questionnaire design and collection contents are as follows:

The choice of questionnaire survey in this paper is mainly based on its ability to effectively collect a wide range of data, while ensuring anonymity and authenticity. Compared with other data collection methods, questionnaire survey can cover a wider audience and get direct feedback on their opinions and behaviors, which is very important for exploring the role of digital platform in corporate environmental protection behavior.

In this paper, the data of environmental behavior and environmental science development released by the United Nations Environment Programme are used as the control data set of questionnaire survey results. Questionnaire survey is the main means to obtain information about environmental behavior and social responsibility of participating enterprises. Siyal et al. ( 43 ) used questionnaires to analyze how inclusive leaders cultivate employees’ innovative work behavior and creativity, and the results showed that inclusive leadership had a positive impact on innovative work behavior and creativity. In this paper, the respondents of private enterprises are divided into three categories: managers (M) who are related to environmental protection behavior and social responsibility activities of enterprises, team members (T) who are responsible for social responsibility, and ordinary employees (N). The sample size is determined based on Cochran formula. Considering the expected effect, α level and statistical power, it is estimated that at least 250 questionnaires are needed to ensure the reliability and representativeness of the research results. Finally, 256 valid questionnaires are collected, which meets the demand of sample size. After the preliminary design of the questionnaire, 20 professionals with relevant backgrounds are invited to fill it out, and the expression and order of the questions are adjusted based on their feedback to improve the clarity and logic of the questionnaire.

In order to ensure the validity and reliability of the questionnaire, this paper refers to the published related research and builds a measurement tool based on the indicators used in these studies. By using the online survey platform to distribute questionnaires and setting up a reminder mechanism, the response rate is improved, and small rewards are provided to participants who complete the questionnaires to ensure the data quality. In order to verify the consistency and reliability of data, Cronbach’s α coefficient and exploratory factor analysis are used for internal consistency test, and Pearson correlation coefficient is also used to evaluate the correlation among variables. The questionnaire is distributed to 297 respondents by e-mail or online survey platform. Two hundred and fifty six valid questionnaires are collected.

The questionnaire is divided into six sections. The first section is basic information statistics, including gender, working years, education level and occupation. The second section is the development level of environmental science, which mainly focuses on the degree of attention paid by enterprises to the development of environmental science and whether enterprises are developing or applying related technologies of environmental science. The third section is the application level of digital platform, knowing the application of digital platform in the enterprise where the interviewee works, including: the experience of using digital platform, whether the enterprise widely uses digital platform to support business operations, and whether the enterprise uses digital platform to monitor and manage data related to environmental protection and social responsibility. The fourth section is the environmental behavior of enterprises, mainly including whether enterprises have taken measures to reduce carbon emissions and whether enterprises actively participate in resource management and sustainable practice. The fifth section investigates the respondents’ questions about CSR activities, and whether they hold positions related to environmental protection or social responsibility, including: whether enterprises actively participate in social responsibility activities, such as charitable donations and community support. Whether the enterprise has social responsibility report or traceable social responsibility record. The sixth section is the intermediary role of digital platform in environmental behavior and social responsibility, mainly including whether enterprises use digital platform to monitor and report environmental behavior and social responsibility activities. In the definition of variables and the construction of measurement scale, this paper defines “corporate social responsibility” as that enterprises voluntarily assume social and environmental responsibilities while pursuing economic benefits. “Digital platform usage” refers to the degree to which enterprises integrate and use digital technology platforms in their operations and management. “Environmental protection behavior” covers all practical actions taken by enterprises to reduce environmental impact and promote sustainable development. The measurement of these variables is based on the previous literature review, combined with expert opinions and pretest results, forming a set of scales containing multiple items, aiming at comprehensively and accurately capturing the core content of each variable. Table 2 shows the definition and selection basis of research variables:

Table 2 . Study the definition and selection basis of variables.

According to the intermediary effect analysis method mentioned by Alfons et al. ( 44 ), Pearson correlation coefficient and Bootstrap method are used in this paper to evaluate the relationship among digital platform usage, CSR policy implementation and corporate environmental behavior. This method is widely recognized and used in social science research, and has been recognized by academic circles for its robustness and applicability. Pearson correlation coefficient is used to analyze the correlation among different variables, and the calculation is shown in Equation (1) :

In Equation (1) , r represents the correlation coefficient. x and y represent two variables respectively, and n represents the sample size. Using Baron and Kenny’s mediation effect analysis method, Equations (2–4) shows the calculation of intermediary effect:

In the above equations, a stands for total effect, b stands for direct effect, c ′ stands for indirect effect, X stands for intermediary variable (application level of digital platform), M stands for the influence of intermediary variable on dependent variable, and Y stands for dependent variable (environmental protection behavior or social responsibility activities of enterprises).

4 Results and discussion

4.1 the results of reliability and validity test and descriptive statistical analysis of the questionnaire.

The reliability and validity of the questionnaire are shown in Figure 4 . It shows that each factor has a high reliability coefficient (greater than 0.84), the factor load (greater than 0.75) indicates that there is a correlation between the problem and each factor, and the KMO value shows that the data is applicable in factor analysis.

Figure 4 . Results of reliability and validity test of questionnaire.

Figure 5 shows the descriptive statistical analysis results of the questionnaire. According to the descriptive statistical results, the respondents’ average scores on policy pressure, market pressure, CSR, environmental performance, and enterprise digital platform level are 4.07, 3.49, 4.27, 3.93, and 4.1, respectively. The evaluation results are relatively consistent. However, there are great differences in the evaluation of public opinion pressure and corporate environmental protection behavior.

Figure 5 . Descriptive statistical analysis results of the questionnaire.

4.2 The correlation between the usage of digital platform and the environmental protection behavior of enterprises

Figure 6 shows the results of correlation analysis between the usage of digital platform and the environmental protection behavior of enterprises. Pearson correlation coefficient shows that there is a moderate positive correlation between the use of digital platforms and corporate environmental behavior (correlation coefficient is 0.523). The Sig. value of correlation analysis is 0.001 (<0.05), which indicates that this correlation is significant. The correlation between the usage of digital platform and enterprise’s environmental behavior is 5.367, Sig. = 0.000 ( p  < 0.05), which verifies hypothesis 1.

Figure 6 . The results of correlation analysis between the use of digital platform and the environmental protection behavior of enterprises.

Figure 7 shows the intermediary analysis of the usage of digital platform. The intermediary analysis shows that the intermediary effect ratio (a * b/c) is 55.31%, and the 95% Bootstrap CI range does not include 0, which indicates that the usage of digital platform plays a significant intermediary role between digital platform and corporate environmental protection behavior.

Figure 7 . Intermediary analysis of the usage degree of digital platform.

4.3 The influence of digital platform on CSR policy and practice

Figure 8 shows the results of correlation analysis between digital platform and CSR. Pearson correlation coefficient shows that there is a moderate positive correlation between the use of digital platforms and CSR policies and practices (correlation coefficient is 0.481). The Sig. value of correlation analysis is 0.003, which is less than the significance level of 0.05, indicating that this correlation is significant. The correlation T between digital platform and CSR is 4.825, Sig. = 0.000 ( p  < 0.05), which shows that there is a positive correlation between digital platform’s social responsibility tools and CSR activities, and supports hypothesis 3.

Figure 8 . Correlation analysis results between digital platform and CSR.

Mediating analysis shows that the mediating effect ratio (a * b/c) is 52.40%, and the 95% Bootstrap CI range does not include 0, indicating that the usage of digital platforms plays a significant mediating role between digital platforms and CSR policies and practices. Figure 9 shows the intermediary analysis of digital platform on CSR policy and practice.

Figure 9 . Intermediary analysis of digital platform on CSR policy and practice.

4.4 Mediating and regulating functions of digital platform and enterprise’s environmental protection behavior

Figure 10 shows the analysis results of the intermediary role and regulatory role of digital platform on enterprise environmental protection behavior. The total effect (a) of digital platform on corporate environmental behavior is 0.627, the total effect (b) of intermediary variable CSR policy implementation is 0.452, and the total effect (b) of intermediary variable environmental innovation technology is 0.313. The mediating effect and 95% confidence interval calculated by Bootstrap method show that the mediating variable CSR policy implementation and environmental protection innovation technology significantly mediate the influence of digital platform on corporate environmental protection behavior, because their confidence intervals do not include 0. T -value and Sig. value also support the significance of these mediating effects. The moderating effect of moderating variable enterprise scale is 0.284, and that of moderating variable industry type is 0.179. The t -value and Sig. value of the regulatory effect show that both the scale of enterprises and the types of industries have a significant regulatory effect on the impact of digital platforms on corporate environmental behavior.

Figure 10 . The analysis results of the mediating and regulating effects of digital platform on enterprise’s environmental protection behavior [ (A) the mediating effect; (B) for regulatory purposes].

In order to further explore the potential causal relationship between the use of digital platforms and the environmental behavior of enterprises, Structural Equation Modeling (SEM) is introduced for analysis. In addition, through the analysis of mediating and moderating effects, it further analyzes how the digital platform affects the CSR practice and environmental behavior of enterprises through different mediating variables (environmental innovation technology) and moderating variables (enterprise scale and industry type). Firstly, a structural equation model is established to evaluate the direct and indirect relationship between digital platform use (independent variable) and enterprise environmental behavior (dependent variable). As a part of indirect relationship, two intermediary variables are considered: CSR policy implementation and environmental innovation technology. Meanwhile, enterprise scale and industry type are regarded as moderating variables to test whether they will change the correlation between the main variables. The hypothesis is tested by multiple regression analysis. This analysis helps to verify the correlation between the use of digital platform, the implementation of CSR policy, environmental innovation technology and corporate environmental behavior, and also examines the regulatory role of enterprise scale and industry type. Table 3 shows the results of multiple regression analysis, which is used to test the direct impact of the use of digital platforms on corporate environmental behavior and its indirect impact through intermediary variables.

Table 3 . Results of SEM and multiple regression analysis.

In Table 3 , the use of digital platform has a significant positive impact on corporate environmental behavior (β = 0.623, p  < 0.001), and CSR policy implementation and environmental innovation technology both show significant positive effects as intermediary variables. In addition, as moderating variables, enterprise scale and industry type have a significant moderating effect on the relationship between the main variables. Through the structural equation model and the results of multiple regression analysis, it is confirmed that there is a significant positive relationship between the use of digital platforms and corporate environmental behavior. Environmental innovation technology and the implementation of CSR policy have played an important intermediary role in this relationship. In addition, the analysis also reveals the moderating role of enterprise scale and industry type in the relationship between digital platform use and enterprise environmental behavior. This emphasizes the need to consider the specific background and characteristics of enterprises when encouraging enterprises to take digital measures to improve their environmental performance. The above findings have important implications for decision makers and policy makers. They emphasize the necessity of supporting enterprises to adopt digital technology to improve environmental protection behavior and CSR practice, and suggest the importance of considering enterprise scale and industry characteristics when designing relevant policies and interventions.

The findings of this paper provide valuable insights for decision makers and policy makers. Firstly, the paper emphasizes the core role of digital platform in promoting corporate environmental behavior and social responsibility practice. The application of digital technology can help enterprises to manage resources more efficiently and formulate environmental protection strategies, thus promoting sustainable development. It is suggested that policy makers should support and encourage enterprises to adopt digital technology to improve their environmental friendliness and social responsibility practice. Secondly, future policy planning needs to take into account the differences in the influence of enterprise scale and industry type on digital platforms. Enterprises of different scales and industries may face different challenges and opportunities in digital transformation, so customized guidelines are needed to guide them to make rational use of digital platforms. Policymakers can formulate targeted policies and measures according to the characteristics of different enterprises to promote the combination of digitalization and sustainable development. Finally, it is suggested that further research should pay attention to the differences in the impact of digital platforms on corporate social responsibility and public health in different regions and cultural backgrounds. Different regions and cultures may have different degrees of acceptance and practice of digitalization, which will have different degrees of impact on corporate social responsibility and public health. In-depth study of the mechanism of digital platforms in different contexts will help to better guide enterprises and policy makers in their decision-making and practice in different environments. Through these suggestions and research directions, people can better promote the goals of corporate social responsibility and sustainable development with the help of digital platforms.

5 Conclusion

The purpose of this paper is to explore the influence of digital platform on corporate environmental behavior and social responsibility, and to deeply understand how digital platform shapes the sustainable development practice of enterprises. Through comprehensive analysis of questionnaire survey data and various research methods, it is found that digital platform plays an active role in the sustainable development of enterprises. There is a positive correlation between the wide application of digital platform and corporate environmental behavior and social responsibility, which shows that digital platform helps enterprises to participate in environmental protection and social responsibility activities more actively and promote sustainable development. Secondly, the environmental protection innovation technology of digital platform has a positive impact on the implementation of environmental protection policies of enterprises. Environmental protection innovation technology plays an intermediary role between digital platform and enterprise environmental protection behavior, which strengthens the influence of digital platform on enterprise environmental protection behavior. In addition, the scale of enterprises and the types of industries plays a regulatory role in the influence mechanism of digital platforms. Enterprises of different scales and industries have different responses to digital platforms, which requires individualized consideration when formulating environmental protection policies and strategies. However, there are some shortcomings in this paper. The research sample has limitations and may not fully represent enterprises of other industries and scales. Future research can expand the sample range, deeply analyze the relationship between digital platform and sustainable development of enterprises, and consider more regulatory factors.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MW: Conceptualization, Data curation, Validation, Writing – review & editing. RY: Conceptualization, Formal analysis, Writing – original draft. XG: Investigation, Methodology, Writing – original draft. ZW: Formal analysis, Methodology, Visualization, Writing – review & editing. YZ: Investigation, Software, Writing – review & editing. TL: Funding acquisition, Project administration, Resources, Software, Supervision, Writing – original draft.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the 2022 Philosophy and Social Science Foundation of Guangdong Province of China (GD22XXW05) entitled “Study on niche selection of Guangdong mainstream media in Guangdong-Hong Kong-Macao Greater Bay Area”, 2018 Social Science Foundation of Guangzhou city of China (2018GZMZYB39) entitled “Research on Guangzhou city brand building and communication strategy under UGC production paradigm” and 2013 Philosophy and Social Science Foundation of Guangdong Province of China (GD13XXW03) entitled “Research on the Reporting Framework of important Health Issues in Guangdong Newspaper Industry.”

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Ansari, NY, Farrukh, M, and Raza, A. Green human resource management and employees pro-environmental behaviours: examining the underlying mechanism. Corp Soc Responsib Environ Manag . (2021) 28:229–38. doi: 10.1002/csr.2044

Crossref Full Text | Google Scholar

2. Sultan, MT, Sharmin, F, Badulescu, A, Stiubea, E, and Xue, K. Travelers’ responsible environmental behavior towards sustainable coastal tourism: an empirical investigation on social media user-generated content. Sustain For . (2021) 13:56. doi: 10.3390/su13010056

3. Sang, Y, and Han, E. A win-win way for corporate and stakeholders to achieve sustainable development: corporate social responsibility value co-creation scale development and validation. Corp Soc Responsib Environ Manag . (2023) 30:1177–90. doi: 10.1002/csr.2412

4. Diez-Cañamero, B, Bishara, T, Otegi-Olaso, JR, Minguez, R, and Fernández, JM. Measurement of corporate social responsibility: a review of corporate sustainability indexes, rankings and ratings. Sustain For . (2020) 12:2153. doi: 10.3390/su12052153

5. Menne, F, Surya, B, Yusuf, M, Suriani, S, Ruslan, M, and Iskandar, I. Optimizing the financial performance of SMEs based on sharia economy: perspective of economic business sustainability and open innovation. J Open Innov: Technol Mark Complex . (2022) 8:18. doi: 10.3390/joitmc8010018

6. Zhuang, M-e, Zhu, W, Huang, L, and Pan, W. Research of influence mechanism of corporate social responsibility for smart cities on consumers' purchasing intention. Library Hi Tech . (2021) 40:1147–58. doi: 10.1108/LHT-11-2020-0290

7. Xu, Y, Wang, L, Xiong, Y, Wang, M, and Xie, X. Does digital transformation foster corporate social responsibility? Evidence from Chinese mining industry. J Environ Manag . (2023) 344:118646. doi: 10.1016/j.jenvman.2023.118646

PubMed Abstract | Crossref Full Text | Google Scholar

8. Orazalin, N. Do board sustainability committees contribute to corporate environmental and social performance? The mediating role of corporate social responsibility strategy. Bus Strateg Environ . (2020) 29:140–53. doi: 10.1002/bse.2354

9. Bialkova, S, and Te Paske, S. Campaign participation, spreading electronic word of mouth, purchase: how to optimise corporate social responsibility, CSR, effectiveness via social media? Eur J Manag Bus Econ . (2021) 30:108–26. doi: 10.1108/EJMBE-08-2020-0244

10. Wang, L, Liu, S, and Xiong, W. The impact of digital transformation on corporate environment performance: evidence from China. Int J Environ Res Public Health . (2022) 19:12846. doi: 10.3390/ijerph191912846

11. Afsar, B, and Umrani, WA. Corporate social responsibility and pro-environmental behavior at workplace: the role of moral reflectiveness, coworker advocacy, and environmental commitment. Corp Soc Responsib Environ Manag . (2020) 27:109–25. doi: 10.1002/csr.1777

12. Raza, A, Farrukh, M, Iqbal, MK, Farhan, M, and Wu, Y. Corporate social responsibility and employees' voluntary pro-environmental behavior: the role of organizational pride and employee engagement. Corp Soc Responsib Environ Manag . (2021) 28:1104–16. doi: 10.1002/csr.2109

13. Latif, B, Ong, TS, Meero, A, Abdul Rahman, AA, and Ali, M. Employee-perceived corporate social responsibility (CSR) and employee pro-environmental behavior (PEB): the moderating role of CSR skepticism and CSR authenticity. Sustain For . (2022) 14:1380. doi: 10.3390/su14031380

14. Deng, Y, Cherian, J, Ahmad, N, Scholz, M, and Samad, S. Conceptualizing the role of target-specific environmental transformational leadership between corporate social responsibility and pro-environmental behaviors of hospital employees. Int J Environ Res Public Health . (2022) 19:3565. doi: 10.3390/ijerph19063565

15. Guan, X, Ahmad, N, Sial, MS, Cherian, J, and Han, H. CSR and organizational performance: the role of pro-environmental behavior and personal values. Corp Soc Responsib Environ Manag . (2023) 30:677–94. doi: 10.1002/csr.2381

16. Giacalone, M, Santarcangelo, V, Donvito, V, Schiavone, O, and Massa, E. Big data for corporate social responsibility: blockchain use in Gioia del Colle DOP. Qual Quant . (2021) 55:1945–71. doi: 10.1007/s11135-021-01095-w

17. Buhmann, A, and Fieseler, C. Deep learning meets deep democracy: deliberative governance and responsible innovation in artificial intelligence. Bus Ethics Q . (2023) 33:146–79. doi: 10.1017/beq.2021.42

18. Li, M. Green governance and corporate social responsibility: the role of big data analytics. Sustain Dev . (2023) 31:773–83. doi: 10.1002/sd.2418

19. Kong, D, and Liu, B. Digital technology and corporate social responsibility: evidence from China. Emerg Mark Financ Trade . (2023) 59:2967–93. doi: 10.1080/1540496X.2023.2199122

20. Li, Q. Evaluation of enterprise financial investment environment based on block-chain and cloud computing. Secur Priv . (2023) 6:e217. doi: 10.1002/spy2.217

21. Najmi, A, Kanapathy, K, and Aziz, AA. Exploring consumer participation in environment management: findings from two-staged structural equation modelling-artificial neural network approach. Corp Soc Responsib Environ Manag . (2021) 28:184–95. doi: 10.1002/csr.2041

22. Yan, C, Siddik, AB, Yong, L, Dong, Q, Zheng, G-W, and Rahman, MN. A two-staged SEM-artificial neural network approach to analyze the impact of FinTech adoption on the sustainability performance of banking firms: the mediating effect of green finance and innovation. Systems . (2022) 10:148. doi: 10.3390/systems10050148

23. Diaz, JF, and Nguyen, TT. Application of grey relational analysis and artificial neural networks on corporate social responsibility (CSR) indices. J Sustain Financ Invest . (2023) 13:1181–99. doi: 10.1080/20430795.2021.1929805

24. Ezzi, F, Jarboui, A, and Mouakhar, K. Exploring the relationship between Blockchain technology and corporate social responsibility performance: empirical evidence from European firms. J Knowl Econ . (2023) 14:1227–48. doi: 10.1007/s13132-022-00946-7

25. Wang, Z, Deng, Y, Zhou, S, and Wu, Z. Achieving sustainable development goal 9: a study of enterprise resource optimization based on artificial intelligence algorithms. Resources Policy . (2023) 80:103212. doi: 10.1016/j.resourpol.2022.103212

26. Wang, Z, Zhang, S, Zhao, Y, Chen, C, and Dong, X. Risk prediction and credibility detection of network public opinion using blockchain technology. Technol Forecast Soc Chang . (2023) 187:122177. doi: 10.1016/j.techfore.2022.122177

27. Deng, Y, Jiang, WY, and Wang, ZY. Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI. Resources Policy . (2023) 82:103522. doi: 10.1016/j.resourpol.2023.103522

28. Li, C, Liang, F, Liang, Y, and Wang, Z. Low-carbon strategy, entrepreneurial activity, and industrial structure change: evidence from a quasi-natural experiment. J Clean Prod . (2023) 427:139183. doi: 10.1016/j.jclepro.2023.139183

29. Li, DD, Guan, X, Tang, TT, Zhao, LY, Tong, WR, and Wang, ZY. The clean energy development path and sustainable development of the ecological environment driven by big data for mining projects. J Environ Manag . (2023) 348:119426. doi: 10.1016/j.jenvman.2023.119426

30. Li, C, Wang, Y, Zhou, Z, Wang, Z, and Mardani, A. Digital finance and enterprise financing constraints: structural characteristics and mechanism identification. J Bus Res . (2023) 165:114074. doi: 10.1016/j.jbusres.2023.114074

31. Li, C, Tang, W, Liang, F, and Wang, Z. The impact of climate change on corporate ESG performance: the role of resource misallocation in enterprises. J Clean Prod . (2024) 445:141263. doi: 10.1016/j.jclepro.2024.141263

32. Sarhan, AA, and Gerged, AM. Do corporate anti-bribery and corruption commitments enhance environmental management performance? The moderating role of corporate social responsibility accountability and executive compensation governance. J Environ Manag . (2023) 341:118063. doi: 10.1016/j.jenvman.2023.118063

33. Karassin, O, and Bar-Haim, A. Multilevel corporate environmental responsibility. J Environ Manag . (2016) 183:110–20. doi: 10.1016/j.jenvman.2016.08.051

34. Li, Y, Zhang, Y, Hu, J, and Wang, Z. Insight into the nexus between intellectual property pledge financing and enterprise innovation: a systematic analysis with multidimensional perspectives. Int Rev Econ Finance . (2024) 93:700–19. doi: 10.1016/j.iref.2024.03.050

35. Wang, Z, Guan, X, Zeng, Y, Liang, X, and Dong, S. Utilizing data platform management to implement “5W” analysis framework for preventing and controlling corruption in grassroots government. Heliyon . (2024) 10:e28601. doi: 10.1016/j.heliyon.2024.e28601

36. Hussain, Y, Abbass, K, Usman, M, Rehan, M, and Asif, M. Exploring the mediating role of environmental strategy, green innovations, and transformational leadership: the impact of corporate social responsibility on environmental performance. Environ Sci Pollut Res . (2022) 29:76864–80. doi: 10.1007/s11356-022-20922-7

37. Abbas, J, and Dogan, E. The impacts of organizational green culture and corporate social responsibility on employees’ responsible behaviour towards the society. Environ Sci Pollut Res . (2022) 29:60024–34. doi: 10.1007/s11356-022-20072-w

38. Zhao, L, Yang, MM, Wang, Z, and Michelson, G. Trends in the dynamic evolution of corporate social responsibility and leadership: a literature review and bibliometric analysis. J Bus Ethics . (2023) 182:135–57. doi: 10.1007/s10551-022-05035-y

39. Dekoninck, H, and Schmuck, D. The mobilizing power of influencers for pro-environmental behavior intentions and political participation. Environ Commun . (2022) 16:458–72. doi: 10.1080/17524032.2022.2027801

40. Bhattacharya, CB, Sen, S, Edinger-Schons, LM, and Neureiter, M. Corporate purpose and employee sustainability behaviors. J Bus Ethics . (2023) 183:963–81. doi: 10.1007/s10551-022-05090-5

41. Pittman, M, Oeldorf-Hirsch, A, and Brannan, A. Green advertising on social media: brand authenticity mediates the effect of different appeals on purchase intent and digital engagement. J Curr Issues Res Advert . (2022) 43:106–21. doi: 10.1080/10641734.2021.1964655

42. Andersen, TCK, Aagaard, A, and Magnusson, M. Exploring business model innovation in SMEs in a digital context: organizing search behaviours, experimentation and decision-making. Creat Innov Manag . (2022) 31:19–34. doi: 10.1111/caim.12474

43. Siyal, S, Xin, C, Umrani, WA, Fatima, S, and Pal, D. How do leaders influence innovation and creativity in employees? The mediating role of intrinsic motivation. Admin Soc . (2021) 53:1337–61. doi: 10.1177/0095399721997427

44. Alfons, A, Ateş, NY, and Groenen, PJF. A robust bootstrap test for mediation analysis. Organ Res Methods . (2021) 25:591–617. doi: 10.1177/1094428121999096

Keywords: digital platform, corporate environmental protection behavior, corporate social responsibility, sustainable development, intermediary analysis

Citation: Wang M, Yuan R, Guan X, Wang Z, Zeng Y and Liu T (2024) The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health. Front. Public Health . 12:1343546. doi: 10.3389/fpubh.2024.1343546

Received: 23 November 2023; Accepted: 03 April 2024; Published: 22 April 2024.

Reviewed by:

Copyright © 2024 Wang, Yuan, Guan, Wang, Zeng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tao Liu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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1 Laboratory of Gene Technology, Department of Biosystems, KU Leuven, Leuven Belgium

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Scientific research progresses by the dialectic dialogue between hypothesis building and the experimental testing of these hypotheses. Microbiologists as biologists in general can rely on an increasing set of sophisticated experimental methods for hypothesis testing such that many scientists maintain that progress in biology essentially comes with new experimental tools. While this is certainly true, the importance of hypothesis building in science should not be neglected. Some scientists rely on intuition for hypothesis building. However, there is also a large body of philosophical thinking on hypothesis building whose knowledge may be of use to young scientists. The present essay presents a primer into philosophical thoughts on hypothesis building and illustrates it with two hypotheses that played a major role in the history of science (the parallel axiom and the fifth element hypothesis). It continues with philosophical concepts on hypotheses as a calculus that fits observations (Copernicus), the need for plausibility (Descartes and Gilbert) and for explicatory power imposing a strong selection on theories (Darwin, James and Dewey). Galilei introduced and James and Poincaré later justified the reductionist principle in hypothesis building. Waddington stressed the feed‐forward aspect of fruitful hypothesis building, while Poincaré called for a dialogue between experiment and hypothesis and distinguished false, true, fruitful and dangerous hypotheses. Theoretical biology plays a much lesser role than theoretical physics because physical thinking strives for unification principle across the universe while biology is confronted with a breathtaking diversity of life forms and its historical development on a single planet. Knowledge of the philosophical foundations on hypothesis building in science might stimulate more hypothesis‐driven experimentation that simple observation‐oriented “fishing expeditions” in biological research.

Short abstract

Scientific research progresses by the dialectic dialogue between hypothesis building and the experimental testing of these hypotheses. Microbiologists can rely on an increasing set of sophisticated experimental methods for hypothesis testing but the importance of hypothesis building in science should not be neglected. This Lilliput offers a primer on philosophical concepts on hypotheses in science.

INTRODUCTION

Philosophy of science and the theory of knowledge (epistemology) are important branches of philosophy. However, philosophy has over the centuries lost its dominant role it enjoyed in antiquity and became in Medieval Ages the maid of theology (ancilla theologiae) and after the rise of natural sciences and its technological applications many practising scientists and the general public doubt whether they need philosophical concepts in their professional and private life. This is in the opinion of the writer of this article, an applied microbiologist, shortsighted for several reasons. Philosophers of the 20th century have made important contributions to the theory of knowledge, and many eminent scientists grew interested in philosophical problems. Mathematics which plays such a prominent role in physics and increasingly also in other branches of science is a hybrid: to some extent, it is the paradigm of an exact science while its abstract aspects are deeply rooted in philosophical thinking. In the present essay, the focus is on hypothesis and hypothesis building in science, essentially it is a compilation what philosophers and scientists thought about this subject in past and present. The controversy between the mathematical mind and that of the practical mind is an old one. The philosopher, physicist and mathematician Pascal ( 1623 –1662a) wrote in his Pensées : “Mathematicians who are only mathematicians have exact minds, provided all things are explained to them by means of definitions and axioms; otherwise they are inaccurate. They are only right when the principles are quite clear. And men of intuition cannot have the patience to reach to first principles of things speculative and conceptional, which they have never seen in the world and which are altogether out of the common. The intellect can be strong and narrow, and can be comprehensive and weak.” Hypothesis building is an act both of intuition and exact thinking and I hope that theoretical knowledge about hypothesis building will also profit young microbiologists.

HYPOTHESES AND AXIOMS IN MATHEMATICS

In the following, I will illustrate the importance of hypothesis building for the history of science and the development of knowledge and illustrate it with two famous concepts, the parallel axiom in mathematics and the five elements hypothesis in physics.

Euclidean geometry

The prominent role of hypotheses in the development of science becomes already clear in the first science book of the Western civilization: Euclid's The Elements written about 300 BC starts with a set of statements called Definitions, Postulates and Common Notions that lay out the foundation of geometry (Euclid,  c.323‐c.283 ). This axiomatic approach is very modern as exemplified by the fact that Euclid's book remained for long time after the Bible the most read book in the Western hemisphere and a backbone of school teaching in mathematics. Euclid's twenty‐three definitions start with sentences such as “1. A point is that which has no part; 2. A line is breadthless length; 3. The extremities of a line are points”; and continues with the definition of angles (“8. A plane angle is the inclination to one another of two lines in a plane which meet one another and do not lie in a straight line”) and that of circles, triangles and quadrilateral figures. For the history of science, the 23rd definition of parallels is particularly interesting: “Parallel straight lines are straight lines which, being in the same plane and being produced indefinitely in both directions, do not meet one another in either direction”. This is the famous parallel axiom. It is clear that the parallel axiom cannot be the result of experimental observations, but must be a concept created in the mind. Euclid ends with five Common Notions (“1. Things which are equal to the same thing are also equal to one another, to 5. The whole is greater than the part”). The establishment of a contradiction‐free system for a branch of mathematics based on a set of axioms from which theorems were deduced was revolutionary modern. Hilbert ( 1899 ) formulated a sound modern formulation for Euclidian geometry. Hilbert's axiom system contains the notions “point, line and plane” and the concepts of “betweenness, containment and congruence” leading to five axioms, namely the axioms of Incidence (“Verknüpfung”), of Order (“Anordnung”), of Congruence, of Continuity (“Stetigkeit”) and of Parallels.

Origin of axioms

Philosophers gave various explanations for the origin of the Euclidean hypotheses or axioms. Plato considered geometrical figures as related to ideas (the true things behind the world of appearances). Aristoteles considered geometric figures as abstractions of physical bodies. Descartes perceived geometric figures as inborn ideas from extended bodies ( res extensa ), while Pascal thought that the axioms of Euclidian geometry were derived from intuition. Kant reasoned that Euclidian geometry represented a priori perceptions of space. Newton considered geometry as part of general mechanics linked to theories of measurement. Hilbert argued that the axioms of mathematical geometry are neither the result of contemplation (“Anschauung”) nor of psychological source. For him, axioms were formal propositions (“formale Aussageformen”) characterized by consistency (“Widerspruchsfreiheit”, i.e. absence of contradiction) (Mittelstrass,  1980a ).

Definitions

Axioms were also differently defined by philosophers. In Topics , Aristoteles calls axioms the assumptions taken up by one partner of a dialogue to initiate a dialectic discussion. Plato states that an axiom needs to be an acceptable or credible proposition, which cannot be justified by reference to other statements. Yet, a justification is not necessary because an axiom is an evident statement. In modern definition, axioms are methodical first sentences in the foundation of a deductive science (Mittelstrass,  1980a ). In Posterior Analytics , Aristotle defines postulates as positions which are at least initially not accepted by the dialogue partners while hypotheses are accepted for the sake of reasoning. In Euclid's book, postulates are construction methods that assure the existence of the geometric objects. Today postulates and axioms are used as synonyms while the 18th‐century philosophy made differences: Lambert defined axioms as descriptive sentences and postulates as prescriptive sentences. According to Kant, mathematical postulates create (synthesize) concepts (Mittelstrass,  1980b ). Definitions then fix the use of signs; they can be semantic definitions that explain the proper meaning of a sign in common language use (in a dictionary style) or they can be syntactic definitions that regulate the use of these signs in formal operations. Nominal definitions explain the words, while real definitions explain the meaning or the nature of the defined object. Definitions are thus essential for the development of a language of science, assuring communication and mutual understanding (Mittelstrass,  1980c ). Finally, hypotheses are also frequently defined as consistent conjectures that are compatible with the available knowledge. The truth of the hypothesis is only supposed in order to explain true observations and facts. Consequences of this hypothetical assumptions should explain the observed facts. Normally, descriptive hypotheses precede explanatory hypotheses in the development of scientific thought. Sometimes only tentative concepts are introduced as working hypotheses to test whether they have an explanatory capacity for the observations (Mittelstrass,  1980d ).

The Euclidian geometry is constructed along a logical “if→then” concept. The “if‐clause” formulates at the beginning the supposition, the “then clause” formulates the consequences from these axioms which provides a system of geometric theorems or insights. The conclusions do not follow directly from the hypothesis; this would otherwise represent self‐evident immediate conclusions. The “if‐then” concept in geometry is not used as in other branches of science where the consequences deduced from the axioms are checked against reality whether they are true, in order to confirm the validity of the hypothesis. The task in mathematics is: what can be logically deduced from a given set of axioms to build a contradiction‐free system of geometry. Whether this applies to the real world is in contrast to the situation in natural sciences another question and absolutely secondary to mathematics (Syntopicon,  1992 ).

Pascal's rules for hypotheses

In his Scientific Treatises on Geometric Demonstrations , Pascal ( 1623‐1662b ) formulates “Five rules are absolutely necessary and we cannot dispense with them without an essential defect and frequently even error. Do not leave undefined any terms at all obscure or ambiguous. Use in definitions of terms only words perfectly well known or already explained. Do not fail to ask that each of the necessary principles be granted, however clear and evident it may be. Ask only that perfectly self‐evident things be granted as axioms. Prove all propositions, using for their proof only axioms that are perfectly self‐evident or propositions already demonstrated or granted. Never get caught in the ambiguity of terms by failing to substitute in thought the definitions which restrict or define them. One should accept as true only those things whose contradiction appears to be false. We may then boldly affirm the original statement, however incomprehensible it is.”

Kant's rules on hypotheses

Kant ( 1724–1804 ) wrote that the analysis described in his book The Critique of Pure Reason “has now taught us that all its efforts to extend the bounds of knowledge by means of pure speculation, are utterly fruitless. So much the wider field lies open to hypothesis; as where we cannot know with certainty, we are at liberty to make guesses and to form suppositions. Imagination may be allowed, under the strict surveillance of reason, to invent suppositions; but these must be based on something that is perfectly certain‐ and that is the possibility of the object. Such a supposition is termed a hypothesis. We cannot imagine or invent any object or any property of an object not given in experience and employ it in a hypothesis; otherwise we should be basing our chain of reasoning upon mere chimerical fancies and not upon conception of things. Thus, we have no right to assume of new powers, not existing in nature and consequently we cannot assume that there is any other kind of community among substances than that observable in experience, any kind of presence than that in space and any kind of duration than that in time. The conditions of possible experience are for reason the only conditions of the possibility of things. Otherwise, such conceptions, although not self‐contradictory, are without object and without application. Transcendental hypotheses are therefore inadmissible, and we cannot use the liberty of employing in the absence of physical, hyperphysical grounds of explanation because such hypotheses do not advance reason, but rather stop it in its progress. When the explanation of natural phenomena happens to be difficult, we have constantly at hand a transcendental ground of explanation, which lifts us above the necessity of investigating nature. The next requisite for the admissibility of a hypothesis is its sufficiency. That is it must determine a priori the consequences which are given in experience and which are supposed to follow from the hypothesis itself.” Kant stresses another aspect when dealing with hypotheses: “It is our duty to try to discover new objections, to put weapons in the hands of our opponent, and to grant him the most favorable position. We have nothing to fear from these concessions; on the contrary, we may rather hope that we shall thus make ourselves master of a possession which no one will ever venture to dispute.”

For Kant's analytical and synthetical judgements and Difference between philosophy and mathematics (Kant, Whitehead) , see Appendices  S1 and S2 , respectively.

Poincaré on hypotheses

The mathematician‐philosopher Poincaré ( 1854 –1912a) explored the foundation of mathematics and physics in his book Science and Hypothesis . In the preface to the book, he summarizes common thinking of scientists at the end of the 19th century. “To the superficial observer scientific truth is unassailable, the logic of science is infallible, and if scientific men sometimes make mistakes, it is because they have not understood the rules of the game. Mathematical truths are derived from a few self‐evident propositions, by a chain of flawless reasoning, they are imposed not only by us, but on Nature itself. This is for the minds of most people the origin of certainty in science.” Poincaré then continues “but upon more mature reflection the position held by hypothesis was seen; it was recognized that it is as necessary to the experimenter as it is to the mathematician. And then the doubt arose if all these constructions are built on solid foundations.” However, “to doubt everything or to believe everything are two equally convenient solutions: both dispense with the necessity of reflection. Instead, we should examine with the utmost care the role of hypothesis; we shall then recognize not only that it is necessary, but that in most cases it is legitimate. We shall also see that there are several kinds of hypotheses; that some are verifiable and when once confirmed by experiment become truths of great fertility; that others may be useful to us in fixing our ideas; and finally that others are hypotheses only in appearance, and reduce to definitions or to conventions in disguise.” Poincaré argues that “we must seek mathematical thought where it has remained pure‐i.e. in arithmetic, in the proofs of the most elementary theorems. The process is proof by recurrence. We first show that a theorem is true for n  = 1; we then show that if it is true for n –1 it is true for n; and we conclude that it is true for all integers. The essential characteristic of reasoning by recurrence is that it contains, condensed in a single formula, an infinite number of syllogisms.” Syllogism is logical argument that applies deductive reasoning to arrive at a conclusion. Poincaré notes “that here is a striking analogy with the usual process of induction. But an essential difference exists. Induction applied to the physical sciences is always uncertain because it is based on the belief in a general order of the universe, an order which is external to us. Mathematical induction‐ i.e. proof by recurrence – is on the contrary, necessarily imposed on us, because it is only the affirmation of a property of the mind itself. No doubt mathematical recurrent reasoning and physical inductive reasoning are based on different foundations, but they move in parallel lines and in the same direction‐namely, from the particular to the general.”

Non‐Euclidian geometry: from Gauss to Lobatschewsky

Mathematics is an abstract science that intrinsically does not request that the structures described reflect a physical reality. Paradoxically, mathematics is the language of physics since the founder of experimental physics Galilei used Euclidian geometry when exploring the laws of the free fall. In his 1623 treatise The Assayer , Galilei ( 1564 –1642a) famously formulated that the book of Nature is written in the language of mathematics, thus establishing a link between formal concepts in mathematics and the structure of the physical world. Euclid's parallel axiom played historically a prominent role for the connection between mathematical concepts and physical realities. Mathematicians had doubted that the parallel axiom was needed and tried to prove it. In Euclidian geometry, there is a connection between the parallel axiom and the sum of the angles in a triangle being two right angles. It is therefore revealing that the famous mathematician C.F. Gauss investigated in the early 19th century experimentally whether this Euclidian theorem applies in nature. He approached this problem by measuring the sum of angles in a real triangle by using geodetic angle measurements of three geographical elevations in the vicinity of Göttingen where he was teaching mathematics. He reportedly measured a sum of the angles in this triangle that differed from 180°. Gauss had at the same time also developed statistical methods to evaluate the accuracy of measurements. Apparently, the difference of his measured angles was still within the interval of Gaussian error propagation. He did not publish the reasoning and the results for this experiment because he feared the outcry of colleagues about this unorthodox, even heretical approach to mathematical reasoning (Carnap,  1891 ‐1970a). However, soon afterwards non‐Euclidian geometries were developed. In the words of Poincaré, “Lobatschewsky assumes at the outset that several parallels may be drawn through a point to a given straight line, and he retains all the other axioms of Euclid. From these hypotheses he deduces a series of theorems between which it is impossible to find any contradiction, and he constructs a geometry as impeccable in its logic as Euclidian geometry. The theorems are very different, however, from those to which we are accustomed, and at first will be found a little disconcerting. For instance, the sum of the angles of a triangle is always less than two right angles, and the difference between that sum and two right angles is proportional to the area of the triangle. Lobatschewsky's propositions have no relation to those of Euclid, but are none the less logically interconnected.” Poincaré continues “most mathematicians regard Lobatschewsky's geometry as a mere logical curiosity. Some of them have, however, gone further. If several geometries are possible, they say, is it certain that our geometry is true? Experiments no doubt teaches us that the sum of the angles of a triangle is equal to two right angles, but this is because the triangles we deal with are too small” (Poincaré,  1854 ‐1912a)—hence the importance of Gauss' geodetic triangulation experiment. Gauss was aware that his three hills experiment was too small and thought on measurements on triangles formed with stars.

Poincaré vs. Einstein

Lobatschewsky's hyperbolic geometry did not remain the only non‐Euclidian geometry. Riemann developed a geometry without the parallel axiom, while the other Euclidian axioms were maintained with the exception of that of Order (Anordnung). Poincaré notes “so there is a kind of opposition between the geometries. For instance the sum of the angles in a triangle is equal to two right angles in Euclid's geometry, less than two right angles in that of Lobatschewsky, and greater than two right angles in that of Riemann. The number of parallel lines that can be drawn through a given point to a given line is one in Euclid's geometry, none in Riemann's, and an infinite number in the geometry of Lobatschewsky. Let us add that Riemann's space is finite, although unbounded.” As further distinction, the ratio of the circumference to the diameter of a circle is equal to π in Euclid's, greater than π in Lobatschewsky's and smaller than π in Riemann's geometry. A further difference between these geometries concerns the degree of curvature (Krümmungsmass k) which is 0 for a Euclidian surface, smaller than 0 for a Lobatschewsky and greater than 0 for a Riemann surface. The difference in curvature can be roughly compared with plane, concave and convex surfaces. The inner geometric structure of a Riemann plane resembles the surface structure of a Euclidean sphere and a Lobatschewsky plane resembles that of a Euclidean pseudosphere (a negatively curved geometry of a saddle). What geometry is true? Poincaré asked “Ought we then, to conclude that the axioms of geometry are experimental truths?” and continues “If geometry were an experimental science, it would not be an exact science. The geometric axioms are therefore neither synthetic a priori intuitions as affirmed by Kant nor experimental facts. They are conventions. Our choice among all possible conventions is guided by experimental facts; but it remains free and is only limited by the necessity of avoiding contradictions. In other words, the axioms of geometry are only definitions in disguise. What then are we to think of the question: Is Euclidean geometry true? It has no meaning. One geometry cannot be more true than another, it can only be more convenient. Now, Euclidean geometry is, and will remain, the most convenient, 1 st because it is the simplest and 2 nd because it sufficiently agrees with the properties of natural bodies” (Poincaré,  1854 ‐1912a).

Poincaré's book was published in 1903 and only a few years later Einstein published his general theory of relativity ( 1916 ) where he used a non‐Euclidean, Riemann geometry and where he demonstrated a structure of space that deviated from Euclidean geometry in the vicinity of strong gravitational fields. And in 1919, astronomical observations during a solar eclipse showed that light rays from a distant star were indeed “bent” when passing next to the sun. These physical observations challenged the view of Poincaré, and we should now address some aspects of hypotheses in physics (Carnap,  1891 ‐1970b).

HYPOTHESES IN PHYSICS

The long life of the five elements hypothesis.

Physical sciences—not to speak of biological sciences — were less developed in antiquity than mathematics which is already demonstrated by the primitive ideas on the elements constituting physical bodies. Plato and Aristotle spoke of the four elements which they took over from Thales (water), Anaximenes (air) and Parmenides (fire and earth) and add a fifth element (quinta essentia, our quintessence), namely ether. Ether is imagined a heavenly element belonging to the supralunar world. In Plato's dialogue Timaios (Plato,  c.424‐c.348 BC a ), the five elements were associated with regular polyhedra in geometry and became known as Platonic bodies: tetrahedron (fire), octahedron (air), cube (earth), icosahedron (water) and dodecahedron (ether). In regular polyhedra, faces are congruent (identical in shape and size), all angles and all edges are congruent, and the same number of faces meet at each vertex. The number of elements is limited to five because in Euclidian space there are exactly five regular polyhedral. There is in Plato's writing even a kind of geometrical chemistry. Since two octahedra (air) plus one tetrahedron (fire) can be combined into one icosahedron (water), these “liquid” elements can combine while this is not the case for combinations with the cube (earth). The 12 faces of the dodecahedron were compared with the 12 zodiac signs (Mittelstrass,  1980e ). This geometry‐based hypothesis of physics had a long life. As late as 1612, Kepler in his Mysterium cosmographicum tried to fit the Platonic bodies into the planetary shells of his solar system model. The ether theory even survived into the scientific discussion of the 19th‐century physics and the idea of a mathematical structure of the universe dominated by symmetry operations even fertilized 20th‐century ideas about symmetry concepts in the physics of elementary particles.

Huygens on sound waves in air

The ether hypothesis figures prominently in the 1690 Treatise on Light from Huygens ( 1617‐1670 ). He first reports on the transmission of sound by air when writing “this may be proved by shutting up a sounding body in a glass vessel from which the air is withdrawn and care was taken to place the sounding body on cotton that it cannot communicate its tremor to the glass vessel which encloses it. After having exhausted all the air, one hears no sound from the metal though it is struck.” Huygens comes up with some foresight when suspecting “the air is of such a nature that it can be compressed and reduced to a much smaller space than that it normally occupies. Air is made up of small bodies which float about and which are agitated very rapidly. So that the spreading of sound is the effort which these little bodies make in collisions with one another, to regain freedom when they are a little more squeezed together in the circuit of these waves than elsewhere.”

Huygens on light waves in ether

“That is not the same air but another kind of matter in which light spreads; since if the air is removed from the vessel the light does not cease to traverse it as before. The extreme velocity of light cannot admit such a propagation of motion” as sound waves. To achieve the propagation of light, Huygens invokes ether “as a substance approaching to perfect hardness and possessing springiness as prompt as we choose. One may conceive light to spread successively by spherical waves. The propagation consists nowise in the transport of those particles but merely in a small agitation which they cannot help communicate to those surrounding.” The hypothesis of an ether in outer space fills libraries of physical discussions, but all experimental approaches led to contradictions with respect to postulated properties of this hypothetical material for example when optical experiments showed that light waves display transversal and not longitudinal oscillations.

The demise of ether

Mechanical models for the transmission of light or gravitation waves requiring ether were finally put to rest by the theory of relativity from Einstein (Mittelstrass,  1980f ). This theory posits that the speed of light in an empty space is constant and does not depend on movements of the source of light or that of an observer as requested by the ether hypothesis. The theory of relativity also provides an answer how the force of gravitation is transmitted from one mass to another across an essentially empty space. In the non‐Euclidian formulation of the theory of relativity (Einstein used the Riemann geometry), there is no gravitation force in the sense of mechanical or electromagnetic forces. The gravitation force is in this formulation simply replaced by a geometric structure (space curvature near high and dense masses) of a four‐dimensional space–time system (Carnap,  1891 ‐1970c; Einstein & Imfeld,  1956 ) Gravitation waves and gravitation lens effects have indeed been experimental demonstrated by astrophysicists (Dorfmüller et al.,  1998 ).

For Aristotle's on physical hypotheses , see Appendix  S3 .

PHILOSOPHICAL THOUGHTS ON HYPOTHESES

In the following, the opinions of a number of famous scientists and philosophers on hypotheses are quoted to provide a historical overview on the subject.

Copernicus' hypothesis: a calculus which fits observations

In his book Revolutions of Heavenly Spheres Copernicus ( 1473–1543 ) reasoned in the preface about hypotheses in physics. “Since the newness of the hypotheses of this work ‐which sets the earth in motion and puts an immovable sun at the center of the universe‐ has already received a great deal of publicity, I have no doubt that certain of the savants have taken great offense.” He defended his heliocentric thesis by stating “For it is the job of the astronomer to use painstaking and skilled observations in gathering together the history of the celestial movements‐ and then – since he cannot by any line of reasoning reach the true causes of these movements‐ to think up or construct whatever causes or hypotheses he pleases such that, by the assumption of these causes, those same movements can be calculated from the principles of geometry for the past and the future too. This artist is markedly outstanding in both of these respects: for it is not necessary that these hypotheses should be true, or even probable; but it is enough if they provide a calculus which fits the observations.” This preface written in 1543 sounds in its arguments very modern physics. However, historians of science have discovered that it was probably written by a theologian friend of Copernicus to defend the book against the criticism by the church.

Bacon's intermediate hypotheses

In his book Novum Organum , Francis Bacon ( 1561–1626 ) claims for hypotheses and scientific reasoning “that they augur well for the sciences, when the ascent shall proceed by a true scale and successive steps, without interruption or breach, from particulars to the lesser axioms, thence to the intermediates and lastly to the most general.” He then notes “that the lowest axioms differ but little from bare experiments, the highest and most general are notional, abstract, and of no real weight. The intermediate are true, solid, full of life, and up to them depend the business and fortune of mankind.” He warns that “we must not then add wings, but rather lead and ballast to the understanding, to prevent its jumping and flying, which has not yet been done; but whenever this takes place we may entertain greater hopes of the sciences.” With respect to methodology, Bacon claims that “we must invent a different form of induction. The induction which proceeds by simple enumeration is puerile, leads to uncertain conclusions, …deciding generally from too small a number of facts. Sciences should separate nature by proper rejections and exclusions and then conclude for the affirmative, after collecting a sufficient number of negatives.”

Gilbert and Descartes for plausible hypotheses

William Gilbert introduced in his book On the Loadstone (Gilbert,  1544‐1603 ) the argument of plausibility into physical hypothesis building. “From these arguments, therefore, we infer not with mere probability, but with certainty, the diurnal rotation of the earth; for nature ever acts with fewer than with many means; and because it is more accordant to reason that the one small body, the earth, should make a daily revolution than the whole universe should be whirled around it.”

Descartes ( 1596‐1650 ) reflected on the sources of understanding in his book Rules for Direction and distinguished what “comes about by impulse, by conjecture, or by deduction. Impulse can assign no reason for their belief and when determined by fanciful disposition, it is almost always a source of error.” When speaking about the working of conjectures he quotes thoughts of Aristotle: “water which is at a greater distance from the center of the globe than earth is likewise less dense substance, and likewise the air which is above the water, is still rarer. Hence, we hazard the guess that above the air nothing exists but a very pure ether which is much rarer than air itself. Moreover nothing that we construct in this way really deceives, if we merely judge it to be probable and never affirm it to be true; in fact it makes us better instructed. Deduction is thus left to us as the only means of putting things together so as to be sure of their truth. Yet in it, too, there may be many defects.”

Care in formulating hypotheses

Locke ( 1632‐1704 ) in his treatise Concerning Human Understanding admits that “we may make use of any probable hypotheses whatsoever. Hypotheses if they are well made are at least great helps to the memory and often direct us to new discoveries. However, we should not take up any one too hastily.” Also, practising scientists argued against careless use of hypotheses and proposed remedies. Lavoisier ( 1743‐1794 ) in the preface to his Element of Chemistry warned about beaten‐track hypotheses. “Instead of applying observation to the things we wished to know, we have chosen rather to imagine them. Advancing from one ill‐founded supposition to another, we have at last bewildered ourselves amidst a multitude of errors. These errors becoming prejudices, are adopted as principles and we thus bewilder ourselves more and more. We abuse words which we do not understand. There is but one remedy: this is to forget all that we have learned, to trace back our ideas to their sources and as Bacon says to frame the human understanding anew.”

Faraday ( 1791–1867 ) in a Speculation Touching Electric Conduction and the Nature of Matter highlighted the fundamental difference between hypotheses and facts when noting “that he has most power of penetrating the secrets of nature, and guessing by hypothesis at her mode of working, will also be most careful for his own safe progress and that of others, to distinguish that knowledge which consists of assumption, by which I mean theory and hypothesis, from that which is the knowledge of facts and laws; never raising the former to the dignity or authority of the latter.”

Explicatory power justifies hypotheses

Darwin ( 1809 –1882a) defended the conclusions and hypothesis of his book The Origin of Species “that species have been modified in a long course of descent. This has been affected chiefly through the natural selection of numerous, slight, favorable variations.” He uses a post hoc argument for this hypothesis: “It can hardly be supposed that a false theory would explain, to so satisfactory a manner as does the theory of natural selection, the several large classes of facts” described in his book.

The natural selection of hypotheses

In the concluding chapter of The Descent of Man Darwin ( 1809 –1882b) admits “that many of the views which have been advanced in this book are highly speculative and some no doubt will prove erroneous.” However, he distinguished that “false facts are highly injurious to the progress of science for they often endure long; but false views do little harm for everyone takes a salutory pleasure in proving their falseness; and when this is done, one path to error is closed and the road to truth is often at the same time opened.”

The American philosopher William James ( 1842–1907 ) concurred with Darwin's view when he wrote in his Principles of Psychology “every scientific conception is in the first instance a spontaneous variation in someone'’s brain. For one that proves useful and applicable there are a thousand that perish through their worthlessness. The scientific conceptions must prove their worth by being verified. This test, however, is the cause of their preservation, not of their production.”

The American philosopher J. Dewey ( 1859‐1952 ) in his treatise Experience and Education notes that “the experimental method of science attaches more importance not less to ideas than do other methods. There is no such thing as experiment in the scientific sense unless action is directed by some leading idea. The fact that the ideas employed are hypotheses, not final truths, is the reason why ideas are more jealously guarded and tested in science than anywhere else. As fixed truths they must be accepted and that is the end of the matter. But as hypotheses, they must be continuously tested and revised, a requirement that demands they be accurately formulated. Ideas or hypotheses are tested by the consequences which they produce when they are acted upon. The method of intelligence manifested in the experimental method demands keeping track of ideas, activities, and observed consequences. Keeping track is a matter of reflective review.”

The reductionist principle

James ( 1842‐1907 ) pushed this idea further when saying “Scientific thought goes by selection. We break the solid plenitude of fact into separate essences, conceive generally what only exists particularly, and by our classifications leave nothing in its natural neighborhood. The reality exists as a plenum. All its part are contemporaneous, but we can neither experience nor think this plenum. What we experience is a chaos of fragmentary impressions, what we think is an abstract system of hypothetical data and laws. We must decompose each chaos into single facts. We must learn to see in the chaotic antecedent a multitude of distinct antecedents, in the chaotic consequent a multitude of distinct consequents.” From these considerations James concluded “even those experiences which are used to prove a scientific truth are for the most part artificial experiences of the laboratory gained after the truth itself has been conjectured. Instead of experiences engendering the inner relations, the inner relations are what engender the experience here.“

Following curiosity

Freud ( 1856–1939 ) considered curiosity and imagination as driving forces of hypothesis building which need to be confronted as quickly as possible with observations. In Beyond the Pleasure Principle , Freud wrote “One may surely give oneself up to a line of thought and follow it up as far as it leads, simply out of scientific curiosity. These innovations were direct translations of observation into theory, subject to no greater sources of error than is inevitable in anything of the kind. At all events there is no way of working out this idea except by combining facts with pure imagination and thereby departing far from observation.” This can quickly go astray when trusting intuition. Freud recommends “that one may inexorably reject theories that are contradicted by the very first steps in the analysis of observation and be aware that those one holds have only a tentative validity.”

Feed‐forward aspects of hypotheses

The geneticist Waddington ( 1905–1975 ) in his essay The Nature of Life states that “a scientific theory cannot remain a mere structure within the world of logic, but must have implications for action and that in two rather different ways. It must involve the consequence that if you do so and so, such and such result will follow. That is to say it must give, or at least offer, the possibility of controlling the process. Secondly, its value is quite largely dependent on its power of suggesting the next step in scientific advance. Any complete piece of scientific work starts with an activity essentially the same as that of an artist. It starts by asking a relevant question. The first step may be a new awareness of some facet of the world that no one else had previously thought worth attending to. Or some new imaginative idea which depends on a sensitive receptiveness to the oddity of nature essentially similar to that of the artist. In his logical analysis and manipulative experimentation, the scientist is behaving arrogantly towards nature, trying to force her into his categories of thought or to trick her into doing what he wants. But finally he has to be humble. He has to take his intuition, his logical theory and his manipulative skill to the bar of Nature and see whether she answers yes or no; and he has to abide by the result. Science is often quite ready to tolerate some logical inadequacy in a theory‐or even a flat logical contradiction like that between the particle and wave theories of matter‐so long as it finds itself in the possession of a hypothesis which offers both the possibility of control and a guide to worthwhile avenues of exploration.”

Poincaré: the dialogue between experiment and hypothesis

Poincaré ( 1854 –1912b) also dealt with physics in Science and Hypothesis . “Experiment is the sole source of truth. It alone can teach us certainty. Cannot we be content with experiment alone? What place is left for mathematical physics? The man of science must work with method. Science is built up of facts, as a house is built of stones, but an accumulation of facts is no more a science than a heap of stones is a house. It is often said that experiments should be made without preconceived concepts. That is impossible. Without the hypothesis, no conclusion could have been drawn; nothing extraordinary would have been seen; and only one fact the more would have been catalogued, without deducing from it the remotest consequence.” Poincaré compares science to a library. Experimental physics alone can enrich the library with new books, but mathematical theoretical physics draw up the catalogue to find the books and to reveal gaps which have to be closed by the purchase of new books.

Poincaré: false, true, fruitful and dangerous hypotheses

Poincaré continues “we all know that there are good and bad experiments. The latter accumulate in vain. Whether there are hundred or thousand, one single piece of work will be sufficient to sweep them into oblivion. Bacon invented the term of an experimentum crucis for such experiments. What then is a good experiment? It is that which teaches us something more than an isolated fact. It is that which enables us to predict and to generalize. Experiments only gives us a certain number of isolated points. They must be connected by a continuous line and that is true generalization. Every generalization is a hypothesis. It should be as soon as possible submitted to verification. If it cannot stand the test, it must be abandoned without any hesitation. The physicist who has just given up one of his hypotheses should rejoice, for he found an unexpected opportunity of discovery. The hypothesis took into account all the known factors which seem capable of intervention in the phenomenon. If it is not verified, it is because there is something unexpected. Has the hypothesis thus rejected been sterile? Far from it. It has rendered more service than a true hypothesis.” Poincaré notes that “with a true hypothesis only one fact the more would have been catalogued, without deducing from it the remotest consequence. It may be said that the wrong hypothesis has rendered more service than a true hypothesis.” However, Poincaré warns that “some hypotheses are dangerous – first and foremost those which are tacit and unconscious. And since we make them without knowing them, we cannot get rid of them.” Poincaré notes that here mathematical physics is of help because by its precision one is compelled to formulate all the hypotheses, revealing also the tacit ones.

Arguments for the reductionist principle

Poincaré also warned against multiplying hypotheses indefinitely: “If we construct a theory upon multiple hypotheses, and if experiment condemns it, which of the premisses must be changed?” Poincaré also recommended to “resolve the complex phenomenon given directly by experiment into a very large number of elementary phenomena. First, with respect to time. Instead of embracing in its entirety the progressive development of a phenomenon, we simply try to connect each moment with the one immediately preceding. Next, we try to decompose the phenomenon in space. We must try to deduce the elementary phenomenon localized in a very small region of space.” Poincaré suggested that the physicist should “be guided by the instinct of simplicity, and that is why in physical science generalization so readily takes the mathematical form to state the problem in the form of an equation.” This argument goes back to Galilei ( 1564 –1642b) who wrote in The Two Sciences “when I observe a stone initially at rest falling from an elevated position and continually acquiring new increments of speed, why should I not believe that such increases take place in a manner which is exceedingly simple and rather obvious to everybody? If now we examine the matter carefully we find no addition or increment more simple than that which repeats itself always in the same manner. It seems we shall not be far wrong if we put the increment of speed as proportional to the increment of time.” With a bit of geometrical reasoning, Galilei deduced that the distance travelled by a freely falling body varies as the square of the time. However, Galilei was not naïve and continued “I grant that these conclusions proved in the abstract will be different when applied in the concrete” and considers disturbances cause by friction and air resistance that complicate the initially conceived simplicity.

Four sequential steps of discovery…

Some philosophers of science attributed a fundamental importance to observations for the acquisition of experience in science. The process starts with accidental observations (Aristotle), going to systematic observations (Bacon), leading to quantitative rules obtained with exact measurements (Newton and Kant) and culminating in observations under artificially created conditions in experiments (Galilei) (Mittelstrass,  1980g ).

…rejected by Popper and Kant

In fact, Newton wrote that he had developed his theory of gravitation from experience followed by induction. K. Popper ( 1902‐1994 ) in his book Conjectures and Refutations did not agree with this logical flow “experience leading to theory” and that for several reasons. This scheme is according to Popper intuitively false because observations are always inexact, while theory makes absolute exact assertions. It is also historically false because Copernicus and Kepler were not led to their theories by experimental observations but by geometry and number theories of Plato and Pythagoras for which they searched verifications in observational data. Kepler, for example, tried to prove the concept of circular planetary movement influenced by Greek theory of the circle being a perfect geometric figure and only when he could not demonstrate this with observational data, he tried elliptical movements. Popper noted that it was Kant who realized that even physical experiments are not prior to theories when quoting Kant's preface to the Critique of Pure Reason : “When Galilei let his globes run down an inclined plane with a gravity which he has chosen himself, then a light dawned on all natural philosophers. They learnt that our reason can only understand what it creates according to its own design; that we must compel Nature to answer our questions, rather than cling to Nature's apron strings and allow her to guide us. For purely accidental observations, made without any plan having been thought out in advance, cannot be connected by a law‐ which is what reason is searching for.” From that reasoning Popper concluded that “we ourselves must confront nature with hypotheses and demand a reply to our questions; and that lacking such hypotheses, we can only make haphazard observations which follow no plan and which can therefore never lead to a natural law. Everyday experience, too, goes far beyond all observations. Everyday experience must interpret observations for without theoretical interpretation, observations remain blind and uninformative. Everyday experience constantly operates with abstract ideas, such as that of cause and effect, and so it cannot be derived from observation.” Popper agreed with Kant who said “Our intellect does not draw its laws from nature…but imposes them on nature”. Popper modifies this statement to “Our intellect does not draw its laws from nature, but tries‐ with varying degrees of success – to impose upon nature laws which it freely invents. Theories are seen to be free creations of our mind, the result of almost poetic intuition. While theories cannot be logically derived from observations, they can, however, clash with observations. This fact makes it possible to infer from observations that a theory is false. The possibility of refuting theories by observations is the basis of all empirical tests. All empirical tests are therefore attempted refutations.”

OUTLOOK: HYPOTHESES IN BIOLOGY

Is biology special.

Waddington notes that “living organisms are much more complicated than the non‐living things. Biology has therefore developed more slowly than sciences such as physics and chemistry and has tended to rely on them for many of its basic ideas. These older physical sciences have provided biology with many firm foundations which have been of the greatest value to it, but throughout most of its history biology has found itself faced with the dilemma as to how far its reliance on physics and chemistry should be pushed” both with respect to its experimental methods and its theoretical foundations. Vitalism is indeed such a theory maintaining that organisms cannot be explained solely by physicochemical laws claiming specific biological forces active in organisms. However, efforts to prove the existence of such vital forces have failed and today most biologists consider vitalism a superseded theory.

Biology as a branch of science is as old as physics. If one takes Aristotle as a reference, he has written more on biology than on physics. Sophisticated animal experiments were already conducted in the antiquity by Galen (Brüssow, 2022 ). Alertus Magnus displayed biological research interest during the medieval time. Knowledge on plants provided the basis of medical drugs in early modern times. What explains biology's decreasing influence compared with the rapid development of physics by Galilei and Newton? One reason is the possibility to use mathematical equations to describe physical phenomena which was not possible for biological phenomena. Physics has from the beginning displayed a trend to few fundamental underlying principles. This is not the case for biology. With the discovery of new continents, biologists were fascinated by the diversity of life. Diversity was the conducting line of biological thinking. This changed only when taxonomists and comparative anatomists revealed recurring pattern in this stunning biological variety and when Darwin provided a theoretical concept to understand variation as a driving force in biology. Even when genetics and molecular biology allowed to understand biology from a few universally shared properties, such as a universal genetic code, biology differed in fundamental aspects from physics and chemistry. First, biology is so far restricted to the planet earth while the laws of physic and chemistry apply in principle to the entire universe. Second, biology is to a great extent a historical discipline; many biological processes cannot be understood from present‐day observations because they are the result of historical developments in evolution. Hence, the importance of Dobzhansky's dictum that nothing makes sense in biology except in the light of evolution. The great diversity of life forms, the complexity of processes occurring in cells and their integration in higher organisms and the importance of a historical past for the understanding of extant organisms, all that has delayed the successful application of mathematical methods in biology or the construction of theoretical frameworks in biology. Theoretical biology by far did not achieve a comparable role as theoretical physics which is on equal foot with experimental physics. Many biologists are even rather sceptical towards a theoretical biology and see progress in the development of ever more sophisticated experimental methods instead in theoretical concepts expressed by new hypotheses.

Knowledge from data without hypothesis?

Philosophers distinguish rational knowledge ( cognitio ex principiis ) from knowledge from data ( cognitio ex data ). Kant associates these two branches with natural sciences and natural history, respectively. The latter with descriptions of natural objects as prominently done with systematic classification of animals and plants or, where it is really history, when describing events in the evolution of life forms on earth. Cognitio ex data thus played a much more prominent role in biology than in physics and explains why the compilation of data and in extremis the collection of museum specimen characterizes biological research. To account for this difference, philosophers of the logical empiricism developed a two‐level concept of science languages consisting of a language of observations (Beobachtungssprache) and a language of theories (Theoriesprache) which are linked by certain rules of correspondence (Korrespondenzregeln) (Carnap,  1891 –1970d). If one looks into leading biological research journals, it becomes clear that biology has a sophisticated language of observation and a much less developed language of theories.

Do we need more philosophical thinking in biology or at least a more vigorous theoretical biology? The breathtaking speed of progress in experimental biology seems to indicate that biology can well develop without much theoretical or philosophical thinking. At the same time, one could argue that some fields in biology might need more theoretical rigour. Microbiologists might think on microbiome research—one of the breakthrough developments of microbiology research in recent years. The field teems with fascinating, but ill‐defined terms (our second genome; holobionts; gut–brain axis; dysbiosis, symbionts; probiotics; health benefits) that call for stricter definitions. One might also argue that biologists should at least consider the criticism of Goethe ( 1749–1832 ), a poet who was also an active scientist. In Faust , the devil ironically teaches biology to a young student.

“Wer will was Lebendigs erkennen und beschreiben, Sucht erst den Geist herauszutreiben, Dann hat er die Teile in seiner Hand, Fehlt, leider! nur das geistige Band.” (To docket living things past any doubt. You cancel first the living spirit out: The parts lie in the hollow of your hand, You only lack the living thing you banned).

We probably need both in biology: more data and more theory and hypotheses.

CONFLICT OF INTEREST

The author reports no conflict of interest.

FUNDING INFORMATION

No funding information provided.

Supporting information

Appendix S1

Brüssow, H. (2022) On the role of hypotheses in science . Microbial Biotechnology , 15 , 2687–2698. Available from: 10.1111/1751-7915.14141 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Bacon, F. (1561. –1626) Novum Organum. In: Adler, M.J. (Ed.) (editor‐in‐chief) Great books of the western world . Chicago, IL: Encyclopaedia Britannica, Inc. 2nd edition 1992 vol 1–60 (abbreviated below as GBWW) here: GBWW vol. 28: 128. [ Google Scholar ]
• Brüssow, H. (2022) What is Truth – in science and beyond . Environmental Microbiology , 24 , 2895–2906. [ PubMed ] [ Google Scholar ]
• Carnap, R. (1891. ‐1970a) Philosophical foundations of physics. Ch. 14 . Basic Books, Inc., New York, 1969. [ Google Scholar ]
• Carnap, R. (1891. ‐1970b) Philosophical foundations of physics. Ch. 15 . Basic Books, Inc., New York, 1969. [ Google Scholar ]
• Carnap, R. (1891. ‐1970c) Philosophical foundations of physics. Ch. 16 . Basic Books, Inc., New York, 1969. [ Google Scholar ]
• Carnap, R. (1891. ‐1970d) Philosophical foundations of physics. Ch. 27–28 . Basic Books, Inc., New York, 1969. [ Google Scholar ]
• Copernicus . (1473. ‐1543) Revolutions of heavenly spheres . GBWW , vol. 15 , 505–506. [ Google Scholar ]
• Darwin, C. (1809. ‐1882a) The origin of species . GBWW , vol. 49 : 239. [ Google Scholar ]
• Darwin, C. (1809. ‐1882b) The descent of man . GBWW , vol. 49 : 590. [ Google Scholar ]
• Descartes, R. (1596. ‐1650) Rules for direction . GBWW , vol. 28 , 245. [ Google Scholar ]
• Dewey, J. (1859. –1952) Experience and education . GBWW , vol. 55 , 124. [ Google Scholar ]
• Dorfmüller, T. , Hering, W.T. & Stierstadt, K. (1998) Bergmann Schäfer Lehrbuch der Experimentalphysik: Band 1 Mechanik, Relativität, Wärme. In: Was ist Schwerkraft: Von Newton zu Einstein . Berlin, New York: Walter de Gruyter, pp. 197–203. [ Google Scholar ]
• Einstein, A. (1916) Relativity . GBWW , vol. 56 , 191–243. [ Google Scholar ]
• Einstein, A. & Imfeld, L. (1956) Die Evolution der Physik . Hamburg: Rowohlts deutsche Enzyklopädie, Rowohlt Verlag. [ Google Scholar ]
• Euclid . (c.323‐c.283) The elements . GBWW , vol. 10 , 1–2. [ Google Scholar ]
• Faraday, M. (1791. –1867) Speculation touching electric conduction and the nature of matter . GBWW , 42 , 758–763. [ Google Scholar ]
• Freud, S. (1856. –1939) Beyond the pleasure principle . GBWW , vol. 54 , 661–662. [ Google Scholar ]
• Galilei, G. (1564. ‐1642a) The Assayer, as translated by S. Drake (1957) Discoveries and Opinions of Galileo pp. 237–8 abridged pdf at Stanford University .
• Galilei, G. (1564. ‐1642b) The two sciences . GBWW vol. 26 : 200. [ Google Scholar ]
• Gilbert, W. (1544. ‐1603) On the Loadstone . GBWW , vol. 26 , 108–110. [ Google Scholar ]
• Goethe, J.W. (1749. –1832) Faust . GBWW , vol. 45 , 20. [ Google Scholar ]
• Hilbert, D. (1899) Grundlagen der Geometrie . Leipzig, Germany: Verlag Teubner. [ Google Scholar ]
• Huygens, C. (1617. ‐1670) Treatise on light . GBWW , vol. 32 , 557–560. [ Google Scholar ]
• James, W. (1842. –1907) Principles of psychology . GBWW , vol. 53 , 862–866. [ Google Scholar ]
• Kant, I. (1724. –1804) Critique of pure reason . GBWW , vol. 39 , 227–230. [ Google Scholar ]
• Lavoisier, A.L. (1743. ‐1794) Element of chemistry . GBWW , vol. 42 , p. 2, 6‐7, 9‐10. [ Google Scholar ]
• Locke, J. (1632. ‐1704) Concerning Human Understanding . GBWW , vol. 33 , 317–362. [ Google Scholar ]
• Mittelstrass, J. (1980a) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 1: 239–241 .
• Mittelstrass, J. (1980b) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 3: 307 .
• Mittelstrass, J. (1980c) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 1: 439–442 .
• Mittelstrass, J. (1980d) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 2: 157–158 .
• Mittelstrass, J. (1980e) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 3: 264‐267, 449.450 .
• Mittelstrass, J. (1980f) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 1: 209–210 .
• Mittelstrass, J. (1980g) Enzyklopädie Philosophie und Wissenschaftstheorie Bibliographisches Institut Mannheim, Wien, Zürich B.I. Wissenschaftsverlag Vol. 1: 281–282 .
• Pascal, B. (1623. ‐1662a) Pensées GBWW vol. 30 : 171–173. [ Google Scholar ]
• Pascal, B. (1623. ‐1662b) Scientific treatises on geometric demonstrations . GBWW vol. 30 : 442–443. [ Google Scholar ]
• Plato . (c.424‐c.348 BC a) Timaeus . GBWW , vol. 6 , 442–477. [ Google Scholar ]
• Poincaré, H. (1854. ‐1912a) Science and hypothesis GBWW , vol. 56 : XV‐XVI, 1–5, 10–15 [ Google Scholar ]
• Poincaré, H. (1854. ‐1912b) Science and hypothesis GBWW , vol. 56 : 40–52. [ Google Scholar ]
• Popper, K. (1902. ‐1994) Conjectures and refutations . London and New York, 2002: The Growth of Scientific Knowledge Routledge Classics, pp. 249–261. [ Google Scholar ]
• Syntopicon . (1992) Hypothesis . GBWW , vol. 1 , 576–587. [ Google Scholar ]
• Waddington, C.H. (1905. –1975) The nature of life . GBWW , vol. 56 , 697–699. [ Google Scholar ]

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Yellowstone’s Wolves: A Debate Over Their Role in the Park’s Ecosystem

New research questions the long-held theory that reintroduction of such a predator caused a trophic cascade, spawning renewal of vegetation and spurring biodiversity.

Yellowstone’s ecological transformation through the reintroduction of wolves has become a case study for how to correct out-of-balance ecosystems. But new research challenges that notion. Credit... Elizabeth Boehm/Danita Delimont, via Alamy

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By Jim Robbins

• April 23, 2024

In 1995, 14 wolves were delivered by truck and sled to the heart of Yellowstone National Park in Wyoming, where the animal had long been absent. Others followed.

Since then, a story has grown up, based on early research, that as the wolves increased in number, they hunted the park’s elk herds, significantly reducing them by about half from 17,000.

The wolves’ return and predatory dominance was believed to have had a widespread effect known as a trophic cascade, by decreasing grazing and restoring and expanding forests, grasses and other wildlife. It supposedly even changed the course of rivers as streamside vegetation returned.

Yellowstone’s dramatic transformation through the reintroduction of wolves has become a global parable for how to correct out-of-balance ecosystems.

In recent years, however, new research has walked that story back. Yes, stands of aspen and willows are thriving again — in some places. But decades of damage from elk herds’ grazing and trampling so thoroughly changed the landscape that large areas remain scarred and may not recover for a long time, if ever.

Wolf packs, in other words, are not magic bullets for restoring ecosystems.

“I would say it’s exaggerated, greatly exaggerated,” said Thomas Hobbs, a professor of natural resource ecology at Colorado State University and the lead author of a long-term study that adds new fuel to the debate over whether Yellowstone experienced a trophic cascade.

“You could argue a trophic trickle maybe,” said Daniel Stahler, the park’s lead wolf biologist who has studied the phenomenon. “Not a trophic cascade.”

Not only is the park’s recovery far less robust than first thought, but the story as it has been told is more complex, Dr. Hobbs said.

But the legend of the wolves’ influence on the park persists.

“How in the world does this lovely story — and it is a beautiful story — come to be seen as fact?” Dr. Hobbs wondered. A chapter of a book tried to answer that, concluding that a video called “ How Wolves Change Rivers ,” which has received tens of millions of views, contributed mightily to the tale.

The ecological record is complicated by the fact that, as elk declined, the number of bison increased substantially, continuing some of the same patterns, like heavy grazing in some places. Moreover, Yellowstone is growing warmer and drier with climate change.

Large numbers of elk in the north of the park had caused significant ecological changes — vegetation disappeared, trampled streams led to extensive erosion, and invasive plant species took hold. Riparian vegetation, or the grasses, the trees and the shrubs along riverbanks and streams, provides a critical habitat for birds, insects and other species to flourish and to maintain biodiversity in the park.

Once elk numbers dwindled, willows and aspens returned along rivers and streams and flourished. The beaver, an engineer of ecosystems, reappeared, using the dense new growth of willows for both food and construction materials. Colonies built new dams, creating ponds that enhanced stream habitats for birds, fish, grizzlies and other bears as well as promoting the growth of more willows and spring vegetation.

But wolves were only one piece of a larger picture, argue Dr. Hobbs and other skeptics of a full-blown trophic cascade at Yellowstone. Grizzly bears and humans played a role, too. For eight years after wolves re-entered the park, hunters killed more elk than the wolves did.

“The other members of the predator guild increased, and human harvest outside of the park has been clearly shown to be responsible for the decline in elk numbers the first 10 years after the wolves were introduced,” Dr. Hobbs said.

The changes attributed to the presence of stalking wolves, some research showed, weren’t only the result of fewer elk, but of a change in elk behavior called “the ecology of fear.” Scientists suggested that the big ungulates could no longer safely hang out along river or stream banks and eat everything in sight. They became extremely cautious, hiding in places where they could be vigilant. That allowed a return of vegetation in those places.

Dr. Hobbs and others contend that subsequent research has not borne that theory out.

Another overlooked factor is that around the same time wolves were returning, 129 beavers were reintroduced by the U.S. Forest Service onto streams north of the park. So it wasn’t just wolf predation on elk and the subsequent return of wolves that enabled an increase in beavers, experts say.

Some researchers say the so-called trophic cascade and rebirth of streamside ecosystems would have been far more robust if it weren’t for the park’s growing bison herd. The bison population is at an all-time high — the most recent count last summer found nearly 5,000 animals. Much larger than elk, bison are less likely to be vulnerable to wolves, which numbered 124 this winter.

The park’s bison, some researchers say, are overgrazing and otherwise seriously damaging the ecosystems — allowing the spread of invasive species and trampling and destroying native plants.

The heavily grazed landscape is why, critics say, some 4,000 bison, also a record, left Yellowstone for Montana in the winter of 2023-24, when an unusually heavy snow buried forage. Because some bison harbor a disease, called brucellosis, that state officials say could infect cattle, they are not welcome outside the park’s borders. (There are no documented cases of transmission between bison and cattle.)

Montana officials say killing animals that may carry disease as they leave the park is the only way to stem the flow. During a hunt that began in the winter of 2023, Native Americans from tribes around the region took part. All told, hunters killed about 1,085 bison; 88 more were shipped to slaughter and 282 were transferred to tribes. This year, just a few animals have left the park.

The Park Service is expected to release a bison management plan in the coming months. It is considering three options: to allow for 3,500 to 5,000 animals, 3,500 to 6,000, or a more natural population that could reach 7,000.

Richard Keigley, who was a research ecologist for the federal Geological Survey in the 1990s, has become an outspoken critic of the park’s bison management.

“They have created this juggernaut where we’ve got thousands of bison and the public believes this is the way things always were,” he said. “The bison that are there now have destroyed and degraded their primary ranges. People have to realize there’s something wrong in Yellowstone.”

Dr. Keigley said the bison population in the park fluctuated in the early years of the park, with about 229 animals in 1967. It has grown steadily since and peaked last year at 5,900.

“There is a hyperabundant bison population in our first national park,” said Robert Beschta, a professor emeritus of forest ecosystems at Oregon State University who has studied Yellowstone riparian areas for 20 years. He pointed to deteriorating conditions along the Lamar River from bison overgrazing.

“They are hammering it,” Mr. Beschta said. “The Lamar ranks right up there with the worst cattle allotments I’ve seen in the American West. Willows can’t grow. Cottonwoods can’t grow.”

A warmer and drier climate, he said, is making matters worse.

Such opinions, however, are not settled science. Some park experts believe that the presence of thousands of bison enhances park habitats because of something called the Green Wave Hypothesis.

Chris Geremia, a park biologist, is an author of a paper that makes the case that a large numbers of bison can stimulate plant growth by grazing grasses to the length of a suburban lawn. “By creating these grazing lawns bison and other herbivores — grasshoppers, elk — these lawns are sustaining more nutritious food for these animals,” he said.

Dr. Geremia contends that a tiny portion — perhaps one-tenth of one percent — of the park may be devoid of some plants. “The other 99.9 percent of those habitats exists in all different levels of willow, aspen and cottonwood,” he said.

The Greater Yellowstone Coalition, a conservation organization, favors a bison population of 4,000 to 6,000 animals. Shana Drimal, who heads the group’s bison conservation program, said that park officials needed to monitor closely changing conditions like climate, drought and bison movement to ensure the ecosystems wouldn’t become further degraded.

Several scientists propose allowing the bison to migrate to the buffer zones beyond the park’s borders, where they are naturally inclined to travel. But it remains controversial because of the threat of disease.

“The only solution is to provide suitable winter range outside the park where they should be tolerated,” said Robert Crabtree, a chief scientist for the Yellowstone Ecological Research Center, a nonprofit. “When they migrate outside the park now it’s to habitat they evolved to prefer — and instead we kill them and ship them away.”

Explore the Animal Kingdom

A selection of quirky, intriguing and surprising discoveries about animal life..

To protect Australia’s iconic animals, scientists are experimenting with vaccine implants , probiotics, tree-planting drones and solar-powered tracking tags.

When traditional conservation fails, science is using “assisted evolution” to give vulnerable wildlife a chance , while posing the question whether we should change species to save them?

Two periodical cicada broods are appearing in a 16-state area in the Midwest and Southeast for the first time in centuries. Can you get rid of them? Do they bite? We answer your questions .

Aside from chimps and humans, researchers have found clear evidence of menopause in only five species — all of them whales. A new study looks at the possible causes for it .

Scientists never imagined that the blind cave salamanders called olms willingly left their caves. Then, they discovered several at aboveground springs in northern Italy .

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RNA's hidden potential: New study unveils its role in early life and future bioengineering

Study sheds light on the molecular evolution of rna and its potential applications in nanobiotechnology..

The beginning of life on Earth and its evolution over billions of years continue to intrigue researchers worldwide. The central dogma or the directional flow of genetic information from a deoxyribose nucleic acid (DNA) template to a ribose nucleic acid (RNA) transcript, and finally into a functional protein, is fundamental to cellular structure and functions. DNA functions as the blueprint of the cell and carries genetic information required for the synthesis of functional proteins. Conversely, proteins are required for the synthesis of DNA. Therefore, whether DNA emerged first or protein, continues to remain a matter of debate.

This molecular version of the "chicken and egg" question led to the proposition of an "RNA World." RNAs in the form of 'ribozymes' or RNA enzymes carry genetic information similar to DNA and also possess catalytic functions like proteins. The discovery of ribozymes further fueled the RNA World hypothesis where RNA served dual functions of "genetic information storage" and "catalysis," facilitating primitive life activities solely by RNA. While modern ribosomes are a complex of RNAs and proteins, ribozymes during early evolutionary stages may have been pieced together through the assembly of individual functional RNA units.

To test this hypothesis, Professor Koji Tamura, along with his team of researchers at the Department of Biological Science and Technology, Tokyo University of Science, conducted a series of experiments to decode the assembly of functional ribozymes. For this, they designed an artificial ribozyme, R3C ligase, to investigate how individual RNA units come together to form a functional structure. Giving further insight into their work published on 17 April 2024, in Life , Prof. Tamura states, "The R3C ligase is a ribozyme that catalyzes the formation of a 3',5'-phosphodiester linkage between two RNA molecules. We modified the structure by adding specific domains that can interact with various effectors."

Within ribosomes, which are the site of protein synthesis, RNA units assemble to function as Peptidyl Transferase Center (PTC) in a way such that they form a scaffold for the recruitment of amino acids (individual components of a peptide/protein) attached to tRNAs. This is an important insight into the evolutionary history of protein synthesis systems, but it is not sufficient to trace the evolutionary pathway based on the RNA World hypothesis.

To explore if the elongation of RNA, achieved by linking individual RNA units together, is regulated allosterically, the researchers altered the structure of the R3C ligase. They did this by incorporating short RNA sequences that bind adenosine triphosphate (ATP), a vital energy carrier molecule in cells, into the ribozyme. The team noted that R3C ligase activity was dependent on the concentration of ATP, with higher activity observed at higher concentrations of ATP. Further, an increase in the melting temperature (T m value) indicated that the binding of ATP to R3C ligase stabilized the structure, which likely influenced its ligase activity.

Similarly, on fusing an L-histidine-binding RNA sequence to the ribozyme, they noted an increase in ligase activity at increasing concentrations of histidine (a key amino acid). Notably, the increase in activity was specific to increasing concentrations of ATP or histidine; no changes were observed in response to other nucleotide triphosphates or amino acids. These findings suggest that ATP and histidine act as effector molecules that trigger structural conformational changes in the ribozyme, which further influence enzyme stability and activity.

ATP is the central energy carrier of the cell which supports numerous molecular processes, while, histidine is the most common amino acid found in the active site of enzymes, and maintains their acid-base chemistry. Given, the important roles of ATP and histidine in RNA interactions and molecular functions, these results provide novel insights into the role of RNA in early evolution, including the origin of the genetic code. Furthermore, engineered ribozymes such as the one developed in this study hold significant promise in a myriad of applications including targeted drug delivery, therapeutics, nano-biosensors, enzyme engineering, and synthesis of novel enzymes with uses in various industrial processes.

Overall, this study can offer insights into how the transition from the RNA World to the modern "DNA/Protein World" occurred. A fundamental understanding of the RNA World in turn, can enhance their use in real-life applications.

"This study will lead to the elucidation of the process of 'allostericity-based acquisition of function and cooperativity' in RNA evolution. The RNA-RNA interactions, RNA-amino acid interactions, and allostericity applied in this research can guide the fabrication of arbitrary RNA nanostructures, with various applications," concludes Prof. Tamura.

• Biochemistry Research
• Cell Biology
• Earth Science
• Origin of Life
• Charles Darwin
• Molecular biology
• Genetic code
• Origin of life

Story Source:

Materials provided by Tokyo University of Science . Note: Content may be edited for style and length.

Journal Reference :

• Yuna Akatsu, Hiromi Mutsuro-Aoki, Koji Tamura. Development of Allosteric Ribozymes for ATP and l-Histidine Based on the R3C Ligase Ribozyme . Life , 2024; 14 (4): 520 DOI: 10.3390/life14040520

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