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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Research Methods Information : Theoretical Models (Using Theory)

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Selected Books on Theory

Selected Journals on Theory

• Historical Materialism Historical Materialism is an interdisciplinary journal dedicated to exploring and developing the critical and explanatory potential of Marxist theory.
• International Journal of Social Research Methodology Focuses on current & emerging methodological debates across a wide range of social science disciplines & substantive interests.
• Journal of Professional Counseling: Practice, Theory & Research Includes practical & unique applications of counseling techniques in schools & clinical settings, as well as significant quantitative & qualitative research.
• Journal of Public Administration Research and Theory Peer reviewed coverage of the research and theory of public administration, including reports of empirical work, and both quantitative and qualitative areas of research.
• Reconceptualizing Educational Research Methodology Reconceptualizing Educational Research Methodology (RERM) is an internationally refereed journal for researchers and practitioners investigating, tracing and theorizing practices, documentations and politics in education.

What is Meant by Theory?

A theory is a well-established principle that has been developed to explain some aspect of the natural world.

Theory of Everything comic by ThadGuy.com and used with permission.

Defining Theory

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge, within the limits of the critical bounding assumptions.

• The theoretical framework is the structure that can hold or support a theory of a research study.
• The theoretical framework introduces and describes the theory which explains why the research problem under study exists.

The Importance of Theory

A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your  research paper and that will relate it to the broader fields of knowledge in the class you are taking.

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

The theoretical framework strengthens the study in the following ways .

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.

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

The Conceptual Framework . College of Education. Alabama State University; Drafting an Argument . Writing@CSU. Colorado State University; Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

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

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

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint (framework) that the researcher will take in analyzing and interpreting the data to be gathered, understanding concepts and variables according to the given definitions, and building knowledge by validating or challenging theoretical assumptions.

II.  Purpose

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

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

*Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Incorporating Theory in Your Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, you are expected to test the validity of an existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism theory, which categorizes perceived differences between nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa.

A test of this theory could be the following: Does Peripheral Realism theory help explain intra-state actions, such as, the growing split between southern and northern Sudan that may likely lead to the creation of two nations?

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

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [I could choose to test Instrumentalist or Circumstantialists models developed among Ethnic Conflict Theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

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

In writing this part of your research paper, keep in mind the following:

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

The Conceptual Framework . College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument . Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. A General Perspective on the Role of Theory in Qualitative Research. Journal of International Social Research 3 (Spring 2010); Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006.

Video on Creating a Theoretical Framework

• Theoretical Framework A short introduction to theoretical frameworks and how to approach constructing one. Presented by Francois J. Desjardins, Associate Professor at University of Ontario Institute of Technology. NOTE: Dr. Desjardins speaks a bit quickly at times but the content of his presentation is very good.

Writing Tip | Borrowing Theoretical Constructs

Borrowing Theoretical Constructs from Elsewhere

A growing and increasingly important trend in the social sciences is to think about and attempt to understand specific research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories you've read about in a particular class, but to think about how an issue might be informed by theories developed in other disciplines.

For example, if you are a political science student studying the rhetorical strategies used by female incumbants in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies.

Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be fully engaged in the research topic.

Writing Tip | Don't Undertheorize

Never leave the theory hanging out there in the Introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you introduce should guide your study throughout the paper.

Be sure to always connect theory to the analysis and to explain in the discussion part of your paper how the theoretical framework you chose fit the research problem, or if appropriate, was inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Writing Tip | Theory vs Hypothesis

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

The terms theory and hypothesis are often used interchangeably in everyday use. However, the difference between them in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world.

Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted [e.g., rational choice theory; grounded theory].

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

The key distinctions are:

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

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

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Implement Hypothesis-Driven Development

Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing  Hypothesis-Driven Development  is thinking about the development of new ideas, products and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behaviour in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning.

Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

• Make observations
• Formulate a hypothesis
• Design an experiment to test the hypothesis
• State the indicators to evaluate if the experiment has succeeded
• Conduct the experiment
• Evaluate the results of the experiment
• Accept or reject the hypothesis
• If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative, and can leverage well understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses.

Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed.

Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection  aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce biased interpretations of the results. 

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will know we have succeeded when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistically significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses when aligned to your MVP can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story

We Believe That increasing the size of hotel images on the booking page

Will Result In improved customer engagement and conversion

We Will Know We Have Succeeded When we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise we are essentially blind to the outcomes of our efforts.

In agile software development we define working software as the primary measure of progress.

By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behaviour. Alternative testings options can be customer surveys, paper prototypes, user and/or guerrilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is  lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared  the story  of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing cost, leaving our competitors in the dust. Ideally we can achieve the ideal of one piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is, before you work on the solution.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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

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

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

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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Hypothesis, Axioms and Models: Types and Examples

Collegedunia Team

Content Curator

Hypothesis, axioms, and models are various concepts that one comes across frequently.

• When one is looking at various relationships, one might come up with many assumptions and theories that will help in all aspects ahead.
• One of the most important things in scientific approaches is such hypotheses, axioms, and models.
• Nowadays these are the formulation of new and improved theories.

Read More: Scientific Investigation

Key Terms: Hypothesis, Axiom, Model, Properties, Types of Hypothesis, Logical and Non-logical axioms, Qualitative and Quantitative Models

What is a Hypothesis?

[Click Here for Sample Questions]

The concept of hypothesis is quite different as compared to axiom. In the case of a hypothesis, one often makes a statement with an assumption. It should be noted here that the hypothesis needs to be proved. 

• A hypothesis is a proposed explanation for a phenomenon.
• There is no requirement that the hypothesis be based on a specific event. 
• So when we talk about a hypothesis pattern, we need to know its origin. For example, if one has a hypothesis formulated in science, the scientific method must test it.
• Scientists base their scientific hypotheses on prior observations that cannot be satisfactorily explained by available scientific theories.
• They usually try to implement various other methods to get a better application that can be put forward for the hypothesis.

Read More: The Scientific Method

Hypothesis Properties

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The following are the properties of the hypothesis:

• It should be empirically tested regardless of whether it is true or false.
• A relationship should be established between the variables that are considered.
• It should be specific, clear, and accurate.
• It should have scope for future studies and should be able to conduct further tests.
• It should be able to be tested in a reasonable time and it should be reliable.

Read More: Types of events

Types of Hypothesis

The hypothesis can be classified as follows:

• Null Hypothesis
• Simple hypothesis
• Directional hypothesis
• Complex hypothesis
• Non-directional hypothesis
• Causal and associative hypothesis

Read More: Scientific Notation Formula

Functions of Hypothesis

The following are the functions of the hypothesis:

• It tells the specific aspects of the study one investigates. 
• It provides a focused study.
• Hypothesis formation leads to an objective in the investigation
• It helps in formulating a theory for the research work and deciding what is right and wrong.
• It filters the data that has to be collected for work.

Read More: Methods of Separation

Hypothesis Examples

Following are some examples of hypotheses:

• An example of a simple hypothesis is that smoking causes cancer.
• If a person works out every day, his/her skin, body, and mind stay healthy and fresh, which is an example of the directional hypothesis.
• If one smokes tobacco it not only causes cancer but also affects the brain, blackens the lips, etc.

Read More: Theoretical Yield Formula

What are Axioms?

First of all, one will try to understand the concept of axiom. The term is derived from the Greek word 'axiom' which means 'true without proof'. Simply put, one can say that an axiom is a mathematical statement that we believe to be true without proof.

• Statements considered here are stand-alone statements and therefore cannot be relied upon by any other statement to prove their truth. 
• Therefore, Axioms can be said to be statements that are known as absolute truths from everyday experiences.
• Here one will also have no experience with statements that contradict them. 
• So when we look at the axioms, one knows that the statements are true and there is no reason to prove the truth. 
• So, in short, an axiom would be a truth that is defined in a statement.

Read More: Exponent Rules

Examples of Axioms

A few examples of axioms are– 

• Two parallel lines never intersect each other.
• All right angles are equal.
• A straight line may be drawn between any two points.
• Probability lies between 0 to 1.
• 0 is a Natural Number.

These are called axioms because one does not need to prove these statements to prove them. So it is clear that this is the proof of the statement itself.

Read More: Uncertainty in Measurement

This is another concept that forms the basis of other statements that have been developed. A model can be defined as a theory proposed to describe an observed phenomenon in a simplified form. 

• In explaining models, one wants to formulate a theory around a process that one sees over and over again so that one can justify the same phenomenon. 
• It can be described as a miniature representation of a larger process. 
• Along with the creation of models, often scientists and researchers try to find the actual mechanism so that they can formulate their actual theories accordingly.
• This concept gives the flexibility to carry out various experiments on prototypes, thus reducing the risk of large-scale implementation. 
• In the case of models, the scientist considers the hypothesis that is formulated first. 
• Then they proceed to the experiment process. 
• After arriving at a particular result, they proceed to build a model to formulate theories accordingly.
• Axioms are assumed truths, and there is no process involved in proving them.
• In the case of hypotheses and models, there is still some room for further experimentation because one has to prove assumptions and then take them to the level of a formulated theory.

Read More: Three States of Matter

Qualitative and Quantitative Models

Both physical and empirical models can be qualitative or quantitative. A qualitative model predicts what behavior you should observe, while a quantitative model predicts the behavior to be observed and the actual values to be measured. The table below will help you understand the different types of models. The number of information increases as one moves from the top left to the bottom right of the table.

Things to Remember

• A hypothesis is a guess without assuming that it is true.
• It should be specific, clear, and accurate. 
• It should have scope for future studies and should be reliable.
• The hypothesis can be classified into six types: Null Hypothesis, Simple hypothesis, Directional hypothesis, Complex hypothesis, Non-directional hypothesis, Causal and associative hypothesis.
• The main sources of the hypothesis are (1) Scientific principles, (2) Personal experience and conclusions followed, (3) Studies that were done in the past, (4) Similarities between phenomena, they are commonly observed patterns (5) General ideas and thinking.
• An axiom is a mathematical statement that we believe to be true without proof.
• A model is a theory proposed to define an observed phenomenon in a simplified form. 
• A qualitative model predicts what behavior one should observe, while a quantitative model predicts the behavior to be observed and the actual values to be measured.

Previous Year Questions

• Electric flux at a point in an electric field is..
• The correct order of acid strength of the following carboxylic acids is.. [Jee Advanced 2019]
• The measurement of voltmeter in the following circuit is... [AIIMS 2017]
• Angular velocity of minute hand of a clock is... [MP PMT 2004]
• Highly pure dilute solution of sodium in liquid ammonia... [Jee Advanced 1998]
• A gas mixture consists of 22 moles of oxygen and 44 moles of Argon at temperature T. Neglecting all vibrational modes, the total internal energy of the system is.. [NEET UG 2017]
• Alkali halides do not show Frenkel defect because​..
• Let R = {(1,3),(4,2),(2,4),(2,3),(3,1)} be a relation on the set A = {1,2,3,4}. The relation R is... [AIEEE 2004]
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Sample Questions

Ques. Why is Euclid's axiom 5 believed to be a ‘universal truth’? (Remember that the equation is not regarding the 5th postulate.) (5 Marks)

Ans: First of all, let us see what axioms and postulates are:

• Axioms: These are assumptions made by Euclid, which were not to be proven, and are used throughout mathematics not specifically related to geometry.
• Postulates: On the other hand, these are conjectures that were unique to geometry only.

This question is about axiom 5 of Euclid. Let us now look at the statement for Euclid's Axiom 5:

It states that 'the whole is always greater than the part.' It defines 'greater than'.

This axiom is known as a universal truth because it is always valid for everything in the universe. We can prove this statement by taking an example. Let's take a mathematical example.

12 + 15 = 27

Here the totality of the numbers 12 and 15 is 27. So, the whole number formed by the addition of two numbers is greater than its individual number or we can say that the parts i.e. 12 and 15 are less than the whole number formed by their sums.

Let us take another example.

Suppose you order a cake on your birthday and you cut a piece of cake with a knife. Then the piece you cut from the whole cake is smaller than the whole cake or only a part of it.

Therefore, we can say that Euclid's axiom 5 is a universal truth.

Ques. Explain the Hypothesis, axioms, and models in brief. (5 Marks)

Ans: The universal law of gravitation proposed by Newton is an assumption or hypothesis, that he proposed with his ingenuity. Before him, there were many observations, experiments, and data on the motion of the planets around the Sun, the motion of the Moon around the Earth, pendulums, bodies falling toward the Earth, etc. Each of these required a different explanation, which was more or less qualitative. What the universal law of gravitation states is that, if we assume that any two bodies in the universe attract each other with a force proportional to the product of their masses and inversely proportionate to the square of the distance between them, then we can define all these statements in one stroke. It not only explains these phenomena but also allows the prediction of the results of future experiments.

A hypothesis is a guess without assuming that it is true. It is not fair to ask someone to prove the universal law of gravitation because it cannot be proven. It can be tested and proven through experiments and observations.

An axiom is a truth while a model is a theory proposed to explain an observed phenomenon. But you need not worry about the nuances of using these words at this stage. For example, next year you will learn about Bohr's model of the hydrogen atom, in which Bohr assumed that the electrons in the hydrogen atom follow certain rules (postulates). Why did he do this? He had before him a vast amount of spectroscopic data which no other theory could explain. So Bohr said that if we assume that atoms behave in this way, we can explain all these things together.

Einstein's special theory of relativity is also based on two assumptions, the constancy of motion of electromagnetic radiation and the validity of physical laws in all inertial frames of reference. It is unwise to ask anyone to prove that the speed of light in a vacuum is constant, independent of source or observer.

Even in mathematics, we need axioms and hypotheses at every stage. Euclid's belief that parallel lines never meet is a hypothesis. This means that if we assume this statement, we can explain many properties of straight lines and two- or three-dimensional figures formed from them. But if you don't assume it, you are free to use a different axiom and gel a new geometry, as has indeed happened in the past few centuries and decades.

Ques. State the difference between a theorem and an axiom. (5 Marks)

Ans: All axioms are theorems. However, theorems include not only axioms but also sentences that can be derived from those axioms by means of inference rules. That is why a formal theory of a language system is a set of its axioms closed under logical consequence.

• An axiom is a statement that is assumed to be true without any proof, while a theory deserves to be proven before it can be assumed to be true or false.
• Axioms are often self-evident, while a theory often requires other statements to be valid, such as other theories and axioms.
• Theorems are inherently more challenging than axioms.
• Basically, theorems are derived from a set of axioms and logical connectives.
• Axioms are the fundamental building blocks of logical or mathematical statements, as they act as the initial points of theorems.
• Axioms can be classified as logical or non-logical.
• The two components of a proof of a theorem are called the hypothesis and the conclusion.

Ques. What are the main steps to developing a scientific theory? (5 Marks)

Ans: Below is the general sequence of steps taken to develop a scientific theory:

• Select and define the natural phenomenon that you need to diagram and describe. Collect data about the phenomenon by investigating the source of the phenomenon and analyzing the observations. We can also replicate this phenomenon by experiment or simulation under a controlled environment (usually inside a laboratory) that removes interference from outside variables.
• After extracting enough data, analyze for recurring patterns in the data. Try to describe this recurring pattern by making a provision explanation (hypothesis).
• Test the hypothesis by getting more information to see if the hypothesis holds true to reveal possible patterns. If the available data does not support the hypothesis, it must be changed or eliminated for the better. During the collection of data, we should not ignore information that contradicts the hypothesis in favor of only supporting information (known as "cherry-picking"). This is often misused by pseudo-scientists who try to deceive people who are not familiar with the scientific method.
• If a concrete hypothesis turns out to be true after all testing and is the most accurate explanation for a phenomenon, it is considered a valid theory. An established theory may undergo modifications and rejection if there is sufficient evidence that contradicts it. Thus, a theory is not an eternal or absolute truth.

Ques. State the difference between an axiom and a postulate. (3 Marks)

Ans: An axiom is a statement, usually considered self-evident, that is assumed to be true without proof. It is used as a starting point in mathematical proofs to deduce other truths.

Classically, axioms were considered separate from laws. An axiom refers to an assumption that is common to many fields of inquiry, while a hypothesis refers to a hypothesis specific to a particular line of inquiry, accepted without evidence. For example, in Euclid's Elements, you can compare "general assumptions" (principles) with conjectures.

In most modern mathematics, however, there is generally no distinction between what is classically known as "axioms" and "postulates". Modern mathematics distinguishes between logical axioms and non-logical axioms, the latter sometimes referred to as postulates.

Postulates are assumptions that are specific to geometry but axioms are assumptions used through mathematics and are not specific to geometry.

Ques. What are the main criteria for formulating a legitimate hypothesis? (5 Marks)

Ans: During the development of a hypothesis, the researcher should not currently have a complete bias on the possible outcome of the test or experiment. 

• It should be reasonably within the scope of the investigation. Then only experimentation or testing increases the likelihood of getting the valid, correct side of the hypothesis.
• If the investigator already knows the result, it is simply considered a "result" (the investigator must have already assumed this during the formulation of the hypothesis).
• If the researcher cannot test the hypotheses through experience or observation, the hypothesis must be tested by other qualified researchers who provide observations.

Researchers investigating alternative hypotheses may consider the following:

• Testability
• Parsimony - discourages the postulation of an unlimited number of entities.
• Scope - Clear application of the hypothesis to many scenarios of phenomena.
• Feasibility – the likelihood that a hypothesis can help explain further events in the future
• Conservatism – Conformity with existing accepted knowledge systems.

Ques. Why is the hypothesis significant? (3 Marks)

Ans: Hypothesis plays an important role in any research project; It is a step towards proving a theory. 

• A hypothesis serves to establish an underlying theory and connection to a particular research topic.
• It helps in processing the data and assesses the reliability and validity of the study.
• It provides the foundation or supporting evidence to demonstrate the validity of the study. 
• A hypothesis allows researchers not only to predict the relationship between variables but also to predict the relationship based on theoretical guidelines and/or empirical evidence.

Ques. How to write a hypothesis? (3 Marks)

Ans: Writing a good hypothesis starts before you start typing. As with many tasks, preparation is very important, so you first start by doing your own analysis and reading everything you can about the topic you decide to research. From there, you will receive the information you need to know, where your focus will be on the subject. Keep in mind that a hypothesis can be a prediction of a relationship that exists between 2 or more variables. The hypothesis should be straightforward and concise, and the conclusion predictable, clear, and without assumptions about the reader's knowledge.

Ques. State a few examples of hypotheses. (2 Marks)

Ans: An example of a simple hypothesis is that taking drugs leads to depression. If a person has a proper diet plan, his skin, body, and mind remain healthy and fresh. This is an example of a directional hypothesis. If you consume drugs it not only induces depression but also impacts your brain, leads to addiction, etc. If you pump petrol in your bike, you can go for longer rides, you also become an expert in riding a bike, you explore more places and encounter new things.

Ques. What is a working hypothesis? (2 Marks)

Ans: A working hypothesis is a type of hypothesis that is scientifically accepted as a basis for further study in the hope that a plausible theory will be obtained, even if the hypothesis ultimately fails. As with most hypotheses, a working hypothesis is formulated as a statement of assumptions, which can be linked to an exploratory investigation objective in an empirical analysis. In qualitative research, they are frequently used as an abstract foundation.

Ques. What is hypothesis testing? What are null and alternative hypotheses? (2 Marks)

Ans: Hypothesis testing is the formal procedure of testing our ideas about the world with the help of statistics . It is used by scientists to test specific predictions, called hypotheses, by calculating whether a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are useful in hypothesis testing in statistics. The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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CBSE CLASS XII Related Questions

1. a constant power is supplied to a rotating disc. the relationship between the angular velocity $\omega$ of the disc and number of rotations (n) made by the disc is governed by.

• $\omega\propto\,n^{\frac{1}{3}}$
• $\omega\propto\,n^{\frac{2}{3}}$
• $\omega\propto\,n^{\frac{3}{2}}$
• $\omega\propto\,n^2$

2. (a) At what distance should the lens be held from the card sheet in Exercise 9.22 in order to view the squares distinctly with the maximum possible magnifying power? (b) What is the magnification in this case? (c) Is the magnification equal to the magnifying power in this case? Explain.

3. a cassegrain telescope uses two mirrors as shown in fig. 9.26. such a telescope is built with the mirrors 20 mm apart. if the radius of curvature of the large mirror is 220 mm and the small mirror is 140 mm, where will the final image of an object at infinity be fig. 9.26, 4. a parallel plate capacitor with air between the plates has a capacitance of 8 pf (1pf = 10 –12 f). what will be the capacitance if the distance between the plates is reduced by half, and the space between them is filled with a substance of dielectric constant 6, 5. a circular disc is rotating about its own axis at uniform angular velocity  \(\omega.\)  the disc is subjected to uniform angular retardation by which its angular velocity is decreased to  \(\frac {\omega}{2}\)  during 120 rotations. the number of rotations further made by it before coming to rest is, 6. light of wavelength 488nm is produced by an argon laser which is used in the photoelectric effect. when light from this spectral line is incident on the emitter, the stopping (cut-off) potential of photoelectrons is 0.38v. find the work function of the material from which the emitter is made., subscribe to our news letter.

Hypothetical Models in Social Science

Cite this chapter.

• Alessandra Basso 2 ,
• Chiara Lisciandra 3 &
• Caterina Marchionni 2  

Part of the book series: Springer Handbooks ((SHB))

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The chapter addresses the philosophical issues raised by the use of hypothetical modeling in the social sciences. Hypothetical modeling involves the construction and analysis of simple hypothetical systems to represent complex social phenomena for the purpose of understanding those social phenomena.

To highlight its main features hypothetical modeling is compared both to laboratory experimentation and to computer simulation. In analogy with laboratory experiments, hypothetical models can be conceived of as scientific representations that attempt to isolate, theoretically, the working of causal mechanisms or capacities from disturbing factors. However, unlike experiments, hypothetical models need to deal with the epistemic uncertainty due to the inevitable presence of unrealistic assumptions introduced for purposes of analytical tractability. Computer simulations have been claimed to be able to overcome some of the strictures of analytical tractability. Still they differ from hypothetical models in how they derive conclusions and in the kind of understanding they provide.

The inevitable presence of unrealistic assumptions makes the legitimacy of the use of hypothetical modeling to learn about the world a particularly pressing problem in the social sciences. A review of the contemporary philosophical debate shows that there is still little agreement on what social scientific models are and what they are for. This suggests that there might not be a single answer to the question of what is the epistemic value of hypothetical models in the social sciences.

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Acknowledgements

Alessandra Basso was mainly responsible for writing Sects.  19.2 and  19.2 , Chiara Lisciandra, Sects.  19.3 and  19.2 , Caterina Marchionni, Sect.  19.1 and Sect.  19.4 . Section  19.4 draws extensively on A. Basso, C. Marchionni: I Modelli in Economia, APhEx (2015). We thank our colleagues at TINT for helpful comments on an earlier draft of the chapter. In particular, we thank Aki Lehtinen, Miles MacLeod, Jaakko Kuorikoski and Till Grüne-Yanoff. Special thanks goes to Juho Pääkkönen for his invaluable assistance. All remaining mistakes are obviously ours.

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Appendix: J.H. von Thünen’s Model of Agricultural Land Use in the Isolated State

Von Thünen ’s model of agricultural land use describes how the distance from a market affects the distribution of agricultural productions around a city [ 19.73 ]. This model is considered to be one of the first examples of modern economic modeling, and it is still a classic model in geography and urban economics from which an entire tradition of models of land use in urban spaces has originated. Von Thünen’s model has also received some attention in the philosophy of economics, and thanks to its analytical simplicity, it is particularly suitable for illustrating some of the ideas discussed in this chapter [ 19.23 , 19.3 , 19.74 ].

Von Thünen’s localization model is based on a set of assumptions that describes a homogeneous and isolated agricultural space in which a single town is located:

The area is a plain, i. e., there are no mountains or valleys

There are no streets or navigable rivers

The plain is completely cut off from the outside world

Climate and fertility are uniform across space

The town is located centrally and has no spatial dimension

All markets and industrial activities take place in the town

Production costs are constant across space

Transportation costs are directly proportional to the distance, the weight and the perishability of the goods

Selling prices are fixed and the demand is unlimited

Farmers have complete information and they act to maximize their revenue.

Under these assumptions, a pattern of concentric rings around the town emerges. Dairying and intensive farming (vegetables and fruit) occupy the ring closest to the town, because these products are perishable and incur the highest transportation costs. Timber and firewood are located in the second ring, because wood is heavy and hence difficult and costly to transport. The third ring consists of extensive farming of crops, such as grain for bread, that are more durable than fruit and less heavy than wood. On the outermost ring stock farming and cattle ranching take place, because animals can walk to the city to be sold at the market and thus have low transportation costs.

This result can also be described in analytical terms by determining which production is most profitable at different distances from the town.

The revenue r of each agricultural production consists in its selling price p minus its production and transportation costs. Since the selling price and the unitary production and transportation costs are fixed, the revenue depends only on the distance from the city

where x is the quantity of the good, c the production cost per unit, t the transportation cost per unit and d the distance from the market. The apex i indicates the kind of agricultural production: Dairying and intensive farming A , timber and firewood B , and extensive farming  C .

The slope of each revenue curve depends on transportation cost and distance − td .

The descending curves in Fig.  19.B4 represent the revenue of each production depending on its distance from the town; e. g., at distance a it becomes more profitable to produce product B .

The production revenue and the land use in von Thünen’s model (after [ 19.75 , p. 76])

Appendix: T. Schelling’s Agent-Based Model of Segregation in Metropolitan Areas

Thomas Schelling’s work on racial segregation paved the way for the use of simulations in the study of social phenomena. In his seminal work, Schelling studied how macro-phenomena, such as segregation, can emerge as an unintended effect of the combination of many interrelated decisions [ 19.76 ]. Racial sorting is a case in point. Segregation has been proven to occur as a side effect of the preference of individuals for having a few neighbors of the same ethnic group, rather than as the consequence of a preference for segregation itself.

Schelling represented the segregation process by means of a checkerboard and dimes and pennies, standing respectively for a certain metropolitan area and for the individuals of two different groups (Fig.  19.C5 ). The model is based on a set of assumptions that describe an idealized metropolitan area and its inhabitants. Examples of such assumptions are:

There are only two kinds of agents, Blacks and Whites

Agents’ decisions only depend on preferences regarding their neighbors

The city is uniform, i. e., there are no architectural or topological boundaries that constrain individual choices

Agents move randomly in space

There are no costs of moving from one point to another.

On the checkerboard it is possible to track the movements of the agents and to observe how the configuration of the neighborhood changes over time.

Schelling’s checkerboard: Initial and final configuration (after [ 19.76 , p. 155–157])

The resulting dynamics reflect the individual decisions to move to areas whose composition meets the agents’ preferences. Rather than obtaining analytical solutions, Schelling’s model explores the conditions under which segregation emerges by means of local rules. What it shows is that, regardless of the initial position of the agents and the spatial configuration, given a certain range of people’s preferences, clusters of neighbors of the same color eventually emerge.

Even though agent-based models do not need to be implemented on a computer, nowadays they are often used together. The premise is to build a model that captures the relevant variables of the agents’ decisions, such as personal preferences and responses to other agents’ behavior and to the context. Next, a way has to be found to implement the model and the other components that characterize the system – such as the network structure – in a computer code. Call the set of relevant factors that are external to the model its environment . Together, the model and the environment constitute the algorithm that runs on the computer.

Figure  19.C6 shows an extract of the algorithm of the segregation model implemented on NetLogo. Each run of the program corresponds to a step in the simulation, which in turn represents a change in the system. The evolution of the system can be represented graphically by means of software that transforms the numerical analysis into visual representations (Fig.  19.C7 ).

Netlogo code of Schelling segregation model (after [ 19.77 ])

Visual representation of Schelling’s segregation model (after [ 19.77 ])

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Basso, A., Lisciandra, C., Marchionni, C. (2017). Hypothetical Models in Social Science. In: Magnani, L., Bertolotti, T. (eds) Springer Handbook of Model-Based Science. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-30526-4_19

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Original research

Evidence-based practice models and frameworks in the healthcare setting: a scoping review, jarrod dusin.

1 Department of Evidence Based Practice, Children’s Mercy Hospitals and Clinics, Kansas City, Missouri, USA

2 Therapeutic Science, The University of Kansas Medical Center, Kansas City, Kansas, USA

Andrea Melanson

Lisa mische-lawson, associated data.

bmjopen-2022-071188supp001.pdf

bmjopen-2022-071188supp002.pdf

No data are available.

The aim of this scoping review was to identify and review current evidence-based practice (EBP) models and frameworks. Specifically, how EBP models and frameworks used in healthcare settings align with the original model of (1) asking the question, (2) acquiring the best evidence, (3) appraising the evidence, (4) applying the findings to clinical practice and (5) evaluating the outcomes of change, along with patient values and preferences and clinical skills.

A Scoping review.

Included sources and articles

Published articles were identified through searches within electronic databases (MEDLINE, EMBASE, Scopus) from January 1990 to April 2022. The English language EBP models and frameworks included in the review all included the five main steps of EBP. Excluded were models and frameworks focused on one domain or strategy (eg, frameworks focused on applying findings).

Of the 20 097 articles found by our search, 19 models and frameworks met our inclusion criteria. The results showed a diverse collection of models and frameworks. Many models and frameworks were well developed and widely used, with supporting validation and updates. Some models and frameworks provided many tools and contextual instruction, while others provided only general process instruction. The models and frameworks reviewed demonstrated that the user must possess EBP expertise and knowledge for the step of assessing evidence. The models and frameworks varied greatly in the level of instruction to assess the evidence. Only seven models and frameworks integrated patient values and preferences into their processes.

Many EBP models and frameworks currently exist that provide diverse instructions on the best way to use EBP. However, the inclusion of patient values and preferences needs to be better integrated into EBP models and frameworks. Also, the issues of EBP expertise and knowledge to assess evidence must be considered when choosing a model or framework.

STRENGTHS AND LIMITATIONS OF THIS STUDY

• Currently, no comprehensive review exists of evidence-based practice (EBP) models and frameworks.
• Well-developed models and frameworks may have been excluded for not including all five steps of original model for EBP.
• This review did not measure the quality of the models and frameworks based on validated studies.

Introduction

Evidence-based practice (EBP) grew from evidence-based medicine (EBM) to provide a process to review, translate and implement research with practice to improve patient care, treatment and outcomes. Guyatt 1 coined the term EBM in the early 1990s. Over the last 25 years, the field of EBM has continued to evolve and is now a cornerstone of healthcare and a core competency for all medical professionals. 2 3 At first, the term EBM was used only in medicine. However, the term EBP now applies to the principles of other health professions. This expansion of the concept of EBM increases its complexity. 4 The term EBP is used for this paper because it is universal across professions.

Early in the development of EBP, Sackett 5 created an innovative five-step model. This foundational medical model provided a concise overview of the process of EBP. The five steps are (1) asking the question, (2) acquiring the best evidence, (3) appraising the evidence, (4) applying the findings to clinical practice and (5) evaluating the outcomes of change. Other critical components of Sackett’s model are considering patient value and preferences and clinical skills with the best available evidence. 5 The influence of this model has led to its integration and adaption into every field of healthcare. Historically, the foundation of EBP has focused on asking the question, acquiring the literature and appraising the evidence but has had difficulty integrating evidence into practice. 6 Although the five steps appear simple, each area includes a vast number of ways to review the literature (eg, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Newcastle-Ottawa Scale) and entire fields of study, such as implementation science, a field dedicated to implementing EBP. 7 8 Implementation science can be traced to the 1960s with Everett Rogers’ Diffusion of Innovation Theory and has grown alongside EBP over the last 25 years. 7 9

One way to manage the complexity of EBP in healthcare is by developing EBP models and frameworks that establish strategies to determine resource needs, identify barriers and facilitators, and guide processes. 10 EBP models and frameworks provide insight into the complexity of transforming evidence into clinical practice. 11 They also allow organisations to determine readiness, willingness and potential outcomes for a hospital system. 12 EBP can differ from implementation science, as EBP models include all five of Sackett’s steps of EBP, while the non-process models of implementation science typically focus on the final two steps. 5 10 There are published scoping reviews of implementation science, 13 however, no comprehensive review of EBP models and frameworks currently exists. Although there is overlap of EBP, implementation science and knowledge translation models and frameworks 10 14 the purpose of the scoping review was to explore how EBP models and frameworks used in healthcare settings align with the original EBP five-step model.

A scoping review synthesises findings across various study types and provides a broad overview of the selected topic. 15 The Arksey and O’Malley method and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) procedures guided this review (see online supplemental PRISMA-ScR checklist ). 15 16 The primary author established the research question and inclusion and exclusion criteria before conducting the review. An a priori protocol was not pre-registered. One research question guided the review: Which EBP models and frameworks align with Sackett’s original model?

Supplementary data

Eligibility criteria.

To be included in the review, English language published EBP models and frameworks needed to include the five main steps of EBP (asking the question, acquiring the best evidence, appraising the evidence, applying the findings to clinical practice and assessing the outcomes of change) based on Sackett’s model. 5 If the models or frameworks involved identifying problems or measured readiness for change, the criteria of ‘asking the question’ was met. Exclusions included models or frameworks focused on one domain or strategy (eg, frameworks focused on applying findings). Also, non-peer-reviewed abstracts, letters, editorials, opinion articles, and dissertations were excluded.

Search and selection

To identify potential studies, a medical librarian searched the databases from January 1990 to April 2022 in MEDLINE, EMBASE and Scopus in collaboration with the primary author. The search was limited to 1990 because the term EBP was coined in the early 90s. The search strategy employed the following keywords: ‘Evidence-Based Practice’ OR ‘evidence based medicine’ OR ‘evidence-based medicine’ OR ‘evidence based nursing’ OR ‘evidence-based nursing’ OR ‘evidence based practice’ OR ‘evidence-based practice’ OR ‘evidence based medicine’ OR ‘evidence-based medicine’ OR ‘evidence based nursing’ OR ‘evidence-based nursing’ OR ‘evidence based practice’ OR ‘evidence-based practice’ AND ‘Hospitals’ OR ‘Hospital Medicine’ OR ‘Nursing’ OR ‘Advanced Practice Nursing’ OR ‘Academic Medical Centers’ OR ‘healthcare’ OR ‘hospital’ OR ‘healthcare’ OR ‘hospital’ AND ‘Models, Organizational’ OR ‘Models, Nursing’ OR ‘framework’ OR ‘theory’ OR ‘theories’ OR ‘model’ OR ‘framework’ OR ‘theory’ OR ‘theories’ OR ‘model’. Additionally, reference lists in publications included for full-text review were screened to identify eligible models and frameworks (see online supplemental appendix A for searches).

Selection of sources of evidence

Two authors (JD and AM) independently screened titles and abstracts and selected studies for potential inclusion in the study, applying the predefined inclusion and exclusion criteria. Both authors then read the full texts of these articles to assess eligibility for final inclusion. Disagreement between the authors regarding eligibility was resolved by consensus between the three authors (JD, AM and LM-L). During the selection process, many models and frameworks were found more than once. Once a model or framework article was identified, the seminal article was reviewed for inclusion. If models or frameworks had been changed or updated since the publication of their seminal article, the most current iteration published was reviewed for inclusion. Once a model or framework was identified and verified for inclusion, all other articles listing the model or framework were excluded. This scoping review intended to identify model or framework aligned with Sackett’s model; therefore, analysing every article that used the included model or framework was unnecessary (see online supplemental appendix B for tracking form).

Data extraction and analysis

Data were collected on the following study characteristics: (1) authors, (2) publication year, (3) model or framework and (4) area(s) of focus in reference to Sackett’s five-step model. After initial selection, models and frameworks were analysed for key features and alignment to the five-step EBP process. A data analysis form was developed to map detailed information (see online supplemental appendix C for full data capture form). Data analysis focused on identifying (1) the general themes of the model or frameworks, and (2) any knowledge gaps. Data extraction and analysis were done by the primary author (JD) and verified by one other author (AM). 15

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

The search identified 6523 potentially relevant references (see figure 1 ). Following a review of the titles and abstracts, the primary author completed a more detailed screening of 37 full papers. From these, 19 models and frameworks were included. Table 1 summarises the 19 models and frameworks. Of the 19 models and frameworks assessed and mapped, 15 had broad target audiences, including healthcare or public health organisations or health systems. Only five models and frameworks included a target audience of individual clinicians (eg, physicians and nurses). 17–22

Retrieval and selection process.

Models and frameworks organised by integration of patient preferences and values

EBP, evidence-based practice.

Asking the question

All 19 models and frameworks included a process for asking questions. Most focused on identifying problems that needed to be addressed on an organisational or hospital level. Five used the PICO (population, intervention, comparator, outcome) format to ask specific questions related to patient care. 19–25

Acquiring the evidence

The models and frameworks gave basic instructions on acquiring literature, such as ‘conduct systematic search’ or ‘acquire resource’. 20 Four recommended sources from previously generated evidence, such as guidelines and systematic reviews. 6 21 22 26 Although most models and frameworks did not provide specifics, others suggested this work be done through EBP mentors/experts. 20 21 25 27 Seven models included qualitative evidence in the use of evidence, 6 19 21 24 27–29 while only four models considered the use of patient preference and values as evidence. 21 22 24 27 Six models recommended internal data be used in acquiring information. 17 20–22 24 27

Assessing the evidence

The models and frameworks varied greatly in the level of instruction provided in assessing the best evidence. All provided a general overview in assessing and grading the evidence. Four recommended this work be done by EBP mentors and experts. 20 25 27 30 Seven models developed specific tools to be used to assess the levels of evidence. 6 17 21 22 24 25 27

Applying the evidence

The application of evidence also varied greatly for the different models and frameworks. Seven models recommended pilot programmes to implement change. 6 21–25 31 Five recommended the use of EBP mentors and experts to assist in the implementation of evidence and quality improvement as a strategy of the models and frameworks. 20 24 25 27 Thirteen models and frameworks discussed patient values and preferences, 6 17–19 21–27 31 32 but only seven incorporated this topic into the model or framework, 21–27 and only five included tools and instructions. 21–25 Twelve of the 20 models discussed using clinical skill, but specifics of how this was incorporated was lacking in models and frameworks. 6 17–19 21–27 31

Evaluating the outcomes of change

Evaluation varied among the models and frameworks, but most involved using implementation outcome measures to determine the project’s success. Five models and frameworks provide tools and in-depth instruction for evaluation. 21 22 24–26 Monash Partners Learning Health Systems provided detailed instruction on using internal institutional data to determine success of application. 26 This framework uses internal and external data along with evidence in decision making as a benchmark for successful implementation.

EBP models and frameworks provide a process for transforming evidence into clinical practice and allow organisations to determine readiness and willingness for change in a complex hospital system. 12 The large number of models and frameworks complicates the process by confusing what the best tool is for healthcare organisations. This review examined many models and frameworks and assessed the characteristics and gaps that can better assist healthcare organisations to determine the right tool for themselves. This review identified 19 EBP models and frameworks that included the five main steps of EBP as described by Sackett. 5 The results showed that the themes of the models and frameworks are as diverse as the models and frameworks themselves. Some are well developed and widely used, with supporting validation and updates. 21 22 24 27 One such model, the Iowa EBP model, has received over 3900 requests for permission to use it and has been updated from its initial development and publication. 24 Other models provided tools and contextual instruction such as the Johns Hopkin’s model which includes a large number of supporting tools for developing PICOs, instructions for grading literature and project implementation. 17 21 22 24 27 By contrast, the ACE Star model and the An Evidence Implementation Model for Public Health Systems only provide high level overview and general instructions compared with other models and frameworks. 19 29 33

Gaps in the evidence

A consistent finding in research of clinician experience with EBP is the lack of expertise that is needed to assess the literature. 24 34 35 The models and frameworks reviewed demonstrated that the user must possess the knowledge and related skills for this step in the process. The models and frameworks varied greatly in the level of instruction to assess the evidence. Most provided a general overview in assessing and grading the evidence, though a few recommended that this work be done by EBP mentors and experts. 20 25 27 ARCC, JBI and Johns Hopkins provided robust tools and resources that would require administrative time and financial support. 21 22 27 Some models and frameworks offered vital resources or pointed to other resources for assessing evidence, 24 but most did not. While a few used mentors and experts to assist with assessing the literature, a majority did not address this persistent issue.

Sackett’s five-step model included another important consideration when implementing EBP: patient values and preferences. One criticism of EBP is that it ignores patient values and preferences. 36 Over half of the models and frameworks reported the need to include patient values and preferences, but the tools, instruction or resources for including them were limited. The ARCC model integrates patient preferences and values into the model, but it is up to the EBP mentor to accomplish this task. 37 There are many tools for assessing evidence, but few models and frameworks provide this level of guidance for incorporating patient preference and values. The inclusion of patient and family values and preferences can be misunderstood, insincere, and even tokenistic but without it there is reduced chance of success of implementation of EBP. 38 39

Strengths and limitations

Similar to other well-designed scoping reviews, the strengths of this review include a rigorous search conducted by a skilled librarian, literature evaluation by more than one person, and the utilisation of an established methodological framework (PRISMA-ScR). 14 15 Additionally, utilising the EBP five-step models as a point of alignment allows for a more comprehensive breakdown and established reference points for the reviewed models and frameworks. While scoping reviews have been completed on implementation science and knowledge translation models and framework, to our knowledge, this is the first scoping review of EBP models and frameworks. 13 14 Limitations of the study include that well-developed models and frameworks may have been excluded for not including all five steps. 40 For example, the Promoting Action on Research Implementation in Health Services (PARIHS) framework is a well-developed and validated implementation framework but did not include all five steps of an EBP model. 40 Also, some models and frameworks have been studied and validated over many years. It was beyond the scope of the review to measure the quality of the models and frameworks based on these other validated studies.

Implications and future research

Healthcare organisations can support EBP by choosing a model or framework that best suits their environment and providing clear guidance for implementing the best evidence. Some organisations may find the best fit with the ARCC and the Clinical Scholars Model because of the emphasis on mentors or the Johns Hopkins model for its tools for grading the level of evidence. 21 25 27 In contrast, other organisations may find the Iowa model useful with its feedback loops throughout its process. 24

Another implication of this study is the opportunity to better define and develop robust tools for patient and family values and preferences within EBP models and frameworks. Patient experiences are complex and require thorough exploration, so it is not overlooked, which is often the case. 39 41 The utilisation of EBP models and frameworks provide an opportunity to explore this area and provide the resources and understanding that are often lacking. 38 Though varying, models such as the Iowa Model, JBI and Johns Hopkins developed tools to incorporate patient and family values and preferences, but a majority of the models and frameworks did not. 21 22 24 An opportunity exists to create broad tools that can incorporate patient and family values and preferences into EBP to a similar extent as many of the models and frameworks used for developing tools for literature assessment and implementation. 21–25

Future research should consider appraising the quality and use of the different EBP models and frameworks to determine success. Additionally, greater clarification on what is considered patient and family values and preferences and how they can be integrated into the different models and frameworks is needed.

This scoping review of 19 models and frameworks shows considerable variation regarding how the EBP models and frameworks integrate the five steps of EBP. Most of the included models and frameworks provided a narrow description of the steps needed to assess and implement EBP, while a few provided robust instruction and tools. The reviewed models and frameworks provided diverse instructions on the best way to use EBP. However, the inclusion of patient values and preferences needs to be better integrated into EBP models. Also, the issues of EBP expertise to assess evidence must be considered when selecting a model or framework.

Supplementary Material

Acknowledgments.

We thank Keri Swaggart for completing the database searches and the Medical Writing Center at Children's Mercy Kansas City for editing this manuscript.

Contributors: All authors have read and approved the final manuscript. JD conceptualised the study design, screened the articles for eligibility, extracted data from included studies and contributed to the writing and revision of the manuscript. LM-L conceptualised the study design, provided critical feedback on the manuscript and revised the manuscript. AM screened the articles for eligibility, extracted data from the studies, provided critical feedback on the manuscript and revised the manuscript. JD is the guarantor of this work.

Funding: The article processing charges related to the publication of this article were supported by The University of Kansas (KU) One University Open Access Author Fund sponsored jointly by the KU Provost, KU Vice Chancellor for Research, and KUMC Vice Chancellor for Research and managed jointly by the Libraries at the Medical Center and KU - Lawrence

Disclaimer: No funding agencies had input into the content of this manuscript.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

ORIGINAL RESEARCH article

A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems.

• Department of Economics, University of Bergamo, Bergamo, Italy

Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.

1. Introduction

Wireless sensor networks (WSNs) enable solutions in multiple fields, and they are adopted in environmental, health, urban, and military applications [ 1 , 2 ]. A problem commonly solved within WSNs is the detection and localization in space of relevant events or targets [ 3 – 7 ].

This study focuses on earthquake early warning systems (EEWSs) [ 8 – 10 ], which are deployed in seismic areas for the real-time detection of earthquakes, with the ultimate goal of sending alerts to citizens and stopping critical processes before ground shaking begins.

Classic EEWSs are based on a dense network of scientific-grade instruments, with construction and operating costs on the order of millions of euros [ 11 ]. This largely limited their implementation, especially in seismic developing countries.

Due to smartphone technology, low-cost EEWSs have been recently implemented at the global level [ 12 ]. Smartphones are used to detect ground shaking using the on-board accelerometer, and a warning is issued to the population as soon as the earthquake is detected. This path has been explored by the Earthquake Network (EQN), a citizen science initiative [ 13 , 14 ], that, since 2013, implements the first smartphone-based EEWS.

Within the EQN EEWS, nodes of the WSN are the smartphones voluntarily made available by citizens. This poses many challenges because personal smartphones mainly sense the “anthropic noise” connected with human activities.

The primary challenge faced by the EQN is to control the probability of false alarms and the probability to miss an earthquake. Alerts may be triggered by events unrelated to earthquakes and some (possibly strong) earthquakes may be missed, especially if the number of monitoring smartphones is small. Both false alarms and missed detections may undermine people's trust in the EQN.

In the pivotal study by Finazzi and Fassò [ 15 ], a statistical methodology is developed for identifying in real-time earthquake occurrence. The study, however, does not take into account the spatial dimension of the smartphone network, making the detection algorithm prone to false alarms. Moreover, the methodology does not allow to estimate important earthquake parameters such as epicenter and depth. In Finazzi et al. [ 16 ], instead, the EQN detection capabilities are modeled within a probabilistic framework. It is discovered that the EQN missed some relatively strong earthquakes that were supposed to be detected by the smartphone network. These considerations and findings suggest that there is room to improve EQN's methods and algorithms.

This study proposes a statistical methodology for 1) controlling the probability of false alarms, 2) controlling the probability of missed detection, 3) classifying a detection between true and false earthquake, and 4) estimating earthquake epicenter and depth (if the detection is classified as a true earthquake).

The methodology is based on a statistical parametric model, statistical hypothesis testing, and Monte Carlo simulation. Contrary to model-less approaches (see for instance [ 3 ]), the methodology exploits the fact that the spatio-temporal dynamic of seismic waves is well-known. This information is retained by the statistical model, and it helps to both classify the EQN detection and to estimate the earthquake parameters.

Due to the peculiarity of the specific application, real-time is a constraint. Ideally, classification and earthquake parameter estimation should not exceed 1 or 2 s of computing time.

The smartphone-based EQN is used to test the statistical methodology, which is then applied to some true and false EQN detections.

2. EQN's detection algorithm

Before formalizing the classification and the earthquake parameter estimation problems, it is useful to detail the output of the earthquake detection algorithm currently implemented by the EQN [ 15 ]. For any given area of radius 30 km, the algorithm compares the number of triggering smartphones in the last 10 s with the number of active smartphones. A triggering smartphone is a smartphone that detected an acceleration above a threshold, while an active smartphone is a smartphone known to monitor earthquakes. If the ratio between triggering smartphones and active smartphones exceeds a threshold, an earthquake is claimed to be detected. The output of the detection algorithm consists of the detection location and the list of the triggering smartphones (triggers for short), which are identified by their spatial coordinates (latitude and longitude) and the triggering time.

3. Problem formalization

An earthquake detection made by an EQN is defined in terms of k j >0 triggers, where j is the index of the generic detection. In general, k j is not a constant, meaning that each detection is characterized by a different number of triggers. Each trigger is described by the feature vector as follows:

where t i ∈ℝ is the triggering time, while ( l a t i , l o n i ) ∈ S 2 are the smartphone coordinates, with S 2 being the sphere embedded in ℝ 3 . The k j ×3 matrix X = ( X 1 ′ , . . . , X k j ′ ) ′ is the data point, and the feature space is X = ∪ k = 1 ∞ X k , with X k = ℝ k × ( S 2 ) k and k > 0 is the generic number of triggers.

Let Y = { - 1 , 1 } ∋ y be the label space. For each earthquake detection, y = 1 if the detection is false while y = −1 if the detection is related to a true earthquake.

The aim is to learn a hypothesis map h : X → Y such that y ≈ h ( X ) for any data point X (i.e., for any future EQN detection). The map h is highly non-linear since the information content of X is determined by the spatio-temporal dynamics of the seismic waves and spatial distribution of the smartphones at the time of the earthquake.

A statistical parametric model f : X → Θ is adopted to understand if X is generated by a true earthquake. The unknown model parameter vector is θ ∈Θ = ℝ s , with s ≪ k j as the vector size. The hypothesis map is then h ( X ) = g ( f ( X )) = g ( θ ). Note that s is constant, and it does not depend on the dimension of X .

When dealing with EEW systems, it is required to control two parameters: the probability α of missed detections (true earthquakes which are not detected by the system) and the probability β of false detections (detections which are not related to any occurred earthquake). It is thus reasonable to adopt a 0/1 loss function as follows:

and to learn a g that minimized the Bayes risk

As discussed by Jung [ 17 ], solving (Equation 1) requires knowing the joint probability distribution p ( X , y ). Instead, we rely on the fact that it is relatively easy to simulate EQN detections under different smartphone geometries and different earthquake parameters. This induces a variability on X and on the number of triggers k j . Assuming to have a data set D = ( X ( 1 ) , y ( 1 ) ) , . . . , ( X ( m ) , y ( m ) ) and that D is a representative sample of p ( X , y ), we define the empirical risk as follows:

and g is learned from the following minimization problem:

Note that solving (Equation 2) is equivalent to solve

where it is made explicit that the probabilities of missed and false detections depend on g .

From an EEW perspective, the solution provided by Equation (3) is not necessarily the best. In some contexts, a missed detection has a larger negative impact than a false detection, while in other contexts, it is the opposite. In this case, one probability is fixed to the desired level, and the other probability is minimized. Two other minimization problems for learning g are the following:

4. Statistical parametric model and classification

In this section, we propose a statistical parametric model for the generic data point X . The observed triggering time for a smartphone sensing an earthquake is modeled as

where t i * is the expected triggering time, while ϵ i ~ N ( 0 , σ ϵ 2 ) is a random component. More in detail

as the distance between the hypocentre and the smartphone location, v is the seismic wave speed, and t O ∈ℝ is the earthquake origin time.

In Equation (8), D i, E is the distance between the epicenter ( l a t E , l o n E ) ∈ S 2 and the smartphone location, d E ∈[0, 500] is the earthquake depth, and R is the earth radius (6, 371 km). Here, it is assumed that all smartphones either detect the primary seismic wave ( v = 7.8 km/s) or they all detect the secondary wave ( v = 4.5 km/s). This assumption is justified by the fact that earthquake detection is based on smartphones within a radius of 30 km, which is a relatively small area.

The role of the random component ϵ i is to model the difference between the expected and the observed triggering time. This difference is mainly due to the smartphone detection delay and a seismic wave velocity that may differ from the expected value.

Equations (6–8) fully define the statistical model f and the model parameter vector is θ = ( l a t E , l o n E , d E , t O , σ ϵ 2 ) ∈ Θ = S 2 × [ 0 , 500 ] × ℝ × ℝ + ⊂ ℝ 6 .

4.1. Model estimation

Model estimation is based on the maximum likelihood method. For a generic EQN detection, the log-likelihood function based on the joint probability distribution of Δ t i = t i - t i * is

The Δ t i are assumed to be independent. This assumption is realistic because smartphones do not share a common clock, detection delays are independent, and the detection by each smartphone is influenced by local factors (e.g., where the smartphone is located, at which floor of the building, and the accelerometer sensitivity).

Maximum likelihood estimates of lat E , lon E , d E , and t O are given by

The solution of Equation (10) cannot be obtained in a closed form due to the non-linearity of Equation (8) hence, estimates are obtained via numerical optimization using the BFGS Quasi-Newton method [ 18 ]. As usual, to avoid local minima, the numerical optimization algorithm is run multiple times starting from random initial values for lat E , lon E , d E , and t O . The minimization in Equation (10) is possible because for any “proposed” values of the model parameters, t i * can be computed using Equations (7), (8) and then compared with the observed t i .

At convergence, the BFGS quasi-network method also returns the Hessian matrix. Since maximum likelihood estimates for model parameters are obtained from a minimization problem, the Hessian is equivalent to the observed Fisher information matrix. The variance–covariance matrix of the three parameters is then the inverse of the Hessian matrix from which standard errors are easily computed.

Finally, the maximum likelihood estimate of the variance is as follows:

where Δ t i ^ = t i - t ^ i * is computed after replacing in Equations (7) and in Equation (8) the maximum likelihood estimates of latitude, longitude, and depth, while μ ^ is the mean of the Δ t i ^ .

4.2. EQN detection classification

Among all elements of θ , the parameter that carries information about how the EQN detection should be classified is σ ϵ 2 . Indeed, σ ^ ϵ 2 tends to be small when the earthquake is true (and triggering times follow the seismic wave dynamic) while σ ^ ϵ 2 tends to be large when the detection is not related to an earthquake event. This implies that g ( θ ) reduces to g ( σ ϵ 2 ) .

In this study, g is chosen to be a statistical hypothesis test on σ ϵ 2 . The system of hypothesis is given by

The null hypothesis is rejected when the variance is higher than expected, namely, when smartphone triggering times do not follow the propagation law of the primary or secondary seismic wave. As customary in the statistical hypothesis testing, the probability α is fixed, and it represents the probability to reject the null hypothesis when it is actually true (namely, it is the probability to miss a true earthquake).

The test statistic is as follows:

which, under the null hypothesis, is distributed as a chi-square with k −4 degrees of freedom ( df ), where 4 is the number of estimated parameters in Equation (10). The null hypothesis is rejected if T ^ > q ( 1 - α ) , d f , where T ^ is obtained replacing σ ϵ 2 with σ ^ ϵ 2 in Equation (13), while q (1−α), df is the (1−α)-quantile of a chi-square distribution with df degrees of freedom, usually called the critical value. In practice, an EQN detection is a true earthquake unless data bring enough evidence that the detection is actually false.

Since we do not know which seismic wave is detected by the smartphones, two models f are estimated: one with v = 7.8 km/s and another with v = 4.5 km/s in Equation (7). This brings to two estimated values for σ ϵ 2 and two hypothesis tests are implemented. The detection is classified as a false earthquake if the null hypothesis is rejected under both tests; otherwise, the earthquake is classified as true.

It is worth noting that the statistical hypothesis test is equivalent to a linear map. Indeed, setting

then g = w ′ϕ, and the earthquake detection classification is based on the following rule:

Finally, δ is obtained by solving the problem

Algorithm 1 summarizes the steps for classifying an EQN detection and for estimating the earthquake parameters in case the detection is classified as a true earthquake.

Algorithm 1 . EQN detection classification and earthquake parameters estimation.

5. Simulation study

The minimization problem in Equation (17) has no closed-form solution. For this reason, we implement a Monte Carlo simulation that aims to simulate a data set D and to minimize Equation (17).

A total of 1,000 true EQN detections and 1,000 false EQN detections are simulated considering the true locations of 1,000 smartphones of the EQN in Lima (Peru).

The probability of missed detection is fixed to α = 0.01 while δ is made varying from 0.1 to 1.5 with step 0.1. For each value of δ, β(δ) is computed by estimating the model f and by implementing the hypothesis test (Equation 13) overall data points X ( j ) in D . Finally, δ ^ is the value of δ that minimizes β(δ).

5.1. Simulation of true detections

For simulating a true earthquake, the following aspects are taken into account: the earthquake epicenter and depth, the arrival time of the seismic wave at the smartphone locations, the earthquake detectability by the smartphone, and the error on the triggering time. Finally, we account for the fact that smartphones may detect events unrelated to the earthquake.

The epicenter locations ( lon E and lat E ) are simulated uniformly inside the coordinates box [−12.39°, −11.74°] for latitude and [−77.17°, −76.66°] for longitude. The box encompasses the EQN of Lima. On the contrary, the earthquake depth is simulated uniformly in the range [0, 100] km independently of the earthquake epicenter.

The arrival time of the seismic wave at each smartphone location is simulated from Equation (6) assuming t O = 0 and v = 7.8 km/s. Only 70% of smartphones are made triggering because of the earthquake. For these smartphones, the error on the triggering time is simulated from a zero mean normal distribution with variance σ ε 2 = 1 . 67 . Such variance guarantees that the 1st and the 99th percentiles of the error distribution are around −3 and 3 s, respectively, which are realistic values for an error on the triggering time.

Of the remaining 30% of smartphones which do not trigger, 6% are made triggering at random with a triggering time uniformly generated in the range [0, 12] s. This implies that when the earthquake is detected by the EQN detection algorithm, the list of triggering smartphones may include triggers unrelated to the earthquake dynamic.

Once the list of triggering smartphones is defined and sorted by triggering time, the EQN detection algorithm is applied to the list. The algorithm stops when the detection condition is satisfied, and the sub-list of triggers that concurred with the earthquake detection is given as the output.

Figure 1 shows an example of a simulated true earthquake. Two separated regions can be visually identified, one with triggering smartphones (those that concurred with the detection) and another with non-triggering smartphones not yet reached by the seismic waves.

Figure 1 . Simulated true earthquake detection based on the EQN smartphone network of Lima (Peru). The diameter of circles is proportional to the triggering time.

5.2. Simulation of false detections

To simulate a false detection, we assume that smartphones trigger at random with a triggering time that does not follow the law of seismic wave propagation. Only 30% of the smartphones are made triggering, and the triggering time is uniformly sampled in the range [0, 12] s.

Figure 2 shows an example of a simulated false EQN detection. Contrary to true earthquakes, no specific spatial pattern on the triggers is observed.

Figure 2 . Simulated false earthquake detection based on the EQN smartphone network of Lima (Peru). The diameter of circles is proportional to the triggering time.

5.3. Simulation results

The minimization of Equation (17) is attained when δ ^ = 0 . 6 and β is found to be equal to 0.008 (conditionally on α = 0.01). Figure 3 shows the empirical distributions of σ ^ ϵ 2 for both true and false simulated EQN detections. Although the detection classification is based on the hypothesis test (and not directly on σ ^ ϵ 2 ), the overlapping between distributions suggests that classification errors are possible.

Figure 3 . Empirical distributions of σ ^ ϵ 2 under simulated true detections (blue histogram) and under simulated false detections (red histogram).

A by-product of detection classification is the estimate of the earthquake parameters. Figure 4 shows the box plots of errors on earthquake epicenter and depth. Both errors have a median of around 18 km, suggesting that along with the detection classification (true/false), the model output can be exploited to provide preliminary estimates of the earthquake parameters.

Figure 4 . Box plot of the errors on epicenter location ( lat E , lon E ) (left) and box plot of the errors on earthquake depth d E (right) for the 1,000 simulated true earthquake detections.

6. Real data example

The methodology developed in this study is applied to true and false detections made by the EQN. As a true earthquake, the event occurred near Genova (Italy) on 4 October 2022 at 21:41:10.5 UTC is considered. Figure 5 depicts the triggering smartphones ( n = 21), while estimation and classification results are reported in Table 1 for v = 7.8 and v = 4.5 km/s, respectively.

Figure 5 . EQN triggers for the earthquake occurred on 4 October 2022 close to Genoa (Italy). The diameter of circles is proportional to the triggering time.

Table 1 . Detection classification and earthquake parameters estimation for the EQN detection near Genova (Italy) assuming v equal to 7.8 and 4.5 km/s.

For both seismic wave velocities, we can observe that latitude and longitude are accurately estimated, while the error in depth is not negligible. Nonetheless, the true values are within the 99% confidence intervals evaluated from the standard errors on the model parameters. In addition, the earthquake is classified as true under both velocities since both observed test statistics are lower than the test critical value. This happens because triggers are close to the epicenter, and primary and secondary seismic waves are nearly concurrent.

The estimation and classification results were obtained in less than 1 s using an Intel(R) Core(TM) i7-9750H CPU @2.60GHz, suggesting that the approach can be adopted for real-time applications.

Figure 6 shows the n = 108 triggers of a false detection occurred near Acapulco (Mexico) on 25 September 2022, at 09:55:45 UTC. In this case, the computed test statistics are 1039.7 and 1026.0 for v = 4.5 and 7.8 km/s, respectively, while the critical value is 141.62. H 0 is rejected in both cases and the detection is claimed as false. In this particular case, the detection was caused by a strong lightning bolt. The speed of sound, however, is around 0.3 km/s, a value much smaller than the speed of primary and secondary seismic waves.

Figure 6 . Triggers for the false EQN detection occurred on 25 September 2022, close to Acapulco (Mexico). The diameter of circles is proportional to the triggering time.

7. Discussion

The methodology developed in this study allows to classify detections made by smartphone-based earthquake early warning systems between true (related to a real earthquake) and false. This is done analyzing the information content of the smartphone triggers that contributed to the detection.

With respect to classic classification problems, the data point describing the triggers has a varying dimension which depends on the smartphone network geometry. The proposed solution is based on two steps. First, a statistical parametric model is used to convert the data point into a parameter vector with a fixed (and small) dimension. Second, a hypothesis test is implemented for classification.

While we do not claim our choices of f and g to be optimal, both steps are based on well-established statistical methods. With respect to the specific choice of g , it is worth discussing that a simpler alternative is the linear map g * = δ′ϕ , with δ = (δ, 1)′ and ϕ = ( 1 , - σ ^ ϵ 2 ) ′ . In this case, the classification is based on the more intuitive comparison σ ^ ϵ 2 ⋛ δ . This simpler solution, however, does not take into account neither the actual number of triggers for the specific detection (10 or 1,000 makes a difference in the uncertainty of σ ^ ϵ 2 ) nor the fact that the distribution of σ ϵ 2 is known under the null hypothesis (that the detection is related to a true earthquake). Using hypothesis testing, we are thus able to retain a part of the information which is lost when X is synthesized with θ .

8. Conclusion

Classification and earthquake parameter estimation are performed in near real time, making the statistical methodology suitable to be implemented in operational systems. On the contrary, the methodology does not fully exploit the information available on the EQN system. Specifically, the modeling is only on the triggering smartphones, while the active non-triggering smartphones are ignored. Knowing, at the EQN detection time, which smartphones have not (yet) triggered may better constraint epicenter and depth, thus improving their estimates.

In addition, for an EEWS like EQN that works globally, it would be important to study if the data set D generated by the Monte Carlo simulation is a representative sample of p ( X , y ). If not, the observed α and β probabilities might deviate from the expected ones.

Finally, a limit of the approach proposed by this study is that the statistical methodology is applied downstream of EQN detections. Ideally, the detection, the classification, and the earthquake parameter estimation problems should be jointly addressed in a unified approach. In this regard, the vast literature on wireless sensor networks may help propose a solution under the real-time constraint.

These open problems, along with the estimation of the earthquake magnitude, will be the focus of future works.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

FF: conceptualization, writing–review, and editing. FM: investigation, methodology, validation, and writing–original draft preparation. All authors contributed to the article and approved the submitted version.

This article was funded by the European Union's Horizon 2020 Research and Innovation Program under grant agreement RISE No. 821115.

Acknowledgments

Authors thank the reviewers and the associate editor for the well-targeted suggestions that considerably improved the quality of the article.

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.

Author disclaimer

Opinions expressed in this article solely reflect the authors' views and the EU is not responsible for any use that may be made of information it contains.

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Keywords: maximum likelihood (ML), Monte Carlo simulation (MC), hypothesis testing (HT), optimization algorithm, classification

Citation: Massoda Tchoussi FY and Finazzi F (2023) A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems. Front. Appl. Math. Stat. 9:1107243. doi: 10.3389/fams.2023.1107243

Received: 24 November 2022; Accepted: 26 January 2023; Published: 16 February 2023.

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Copyright © 2023 Massoda Tchoussi and Finazzi. 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.

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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Health professionals’ acceptance of mobile-based clinical guideline application in a resource-limited setting: using a modified UTAUT model

• Addisalem Workie Demsash 1 ,
• Mulugeta Hayelom Kalayou 2 &
• Agmasie Damtew Walle 1  

BMC Medical Education volume  24 , Article number:  689 ( 2024 ) Cite this article

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Introduction

Clinical guidelines are crucial for assisting health professionals to make correct clinical decisions. However, manual clinical guidelines are not accessible, and this increases the workload. So, a mobile-based clinical guideline application is needed to provide real-time information access. Hence, this study aimed to assess health professionals’ intention to accept mobile-based clinical guideline applications and verify the unified theory of acceptance and technology utilization model.

Institutional-based cross-sectional study design was used among 803 study participants. The sample size was determined based on structural equation model parameter estimation criteria with stratified random sampling. Amos version 23 software was used for analysis. Internal consistency of latent variable items, and convergent and divergent validity, were evaluated using composite reliability, AVE, and a cross-loading matrix. Model fitness of the data was assessed based on a set of criteria, and it was achieved. P-value < 0.05 was considered for assessing the formulated hypothesis.

Effort expectancy and social influence had a significant effect on health professionals’ attitudes, with path coefficients of ( β = 0.61, P-value < 0.01 ), and ( β = 0.510, P-value < 0.01 ) respectively. Performance expectancy, facilitating condition, and attitude had significant effects on health professionals’ acceptance of mobile-based clinical guideline applications with path coefficients of ( β = 0.37, P-value < 0.001 ), ( β = 0.44, P-value < 0.001 ) and ( β = 0.57, P-value < 0.05 ) respectively. Effort expectancy and social influence were mediated by attitude and had a significant partial relationship with health professionals’ acceptance of mobile-based clinical guideline application with standardized estimation coefficients of ( β = 0.22, P-value = 0.027 ), and ( β = 0.19, P-value = 0.031 ) respectively. All the latent variables accounted for 57% of health professionals’ attitudes, and latent variables with attitudes accounted for 63% of individuals’ acceptance of mobile-based clinical guideline applications.

Conclusions

The unified theory of acceptance and use of the technology model was a good model for assessing individuals’ acceptance of mobile-based clinical guidelines applications. So, enhancing health professionals’ attitudes, and computer literacy through training are needed. Mobile application development based on user requirements is critical for technology adoption, and people’s support is also important for health professionals to accept and use the application.

Peer Review reports

Clinical practice guidelines are methodically developed statements to assist health professionals and patients’ decisions about suitable healthcare for specific clinical conditions. When it comes to a particular therapy, diagnosis, and pharmaceutical processes in patient care, clinical practice guidelines play a major role [ 1 ]. The medical guideline isn’t a fixed protocol that must be followed; it is also a recommendation for healthcare professionals to consider for correct patient diagnosis and treatment [ 2 ], as well as a written document that swiftly offers technical assistance, advice on the definition and operationalization of medical terms, and certain aspects of planning for implementation and evaluation [ 3 ].

A clinical guideline has several benefits and opportunities for healthcare practitioners, institutions, and patients. It enhances health professionals’ communications and evidence-based practice [ 4 , 5 , 6 ]. It serves as the same standard in all health institutions for diagnosis and treatment to ensure the consistency of patient care and is critical for quality audits and evaluations [ 7 ]. Plus, clinical guidelines are part of the work of health professionals’ consultants and are fertile for the care of patients as references for health professionals to access the right information when and where needed.

Additionally, well-trained health professionals are not equally accessible in all health institutions in low-income countries; their educational and training qualifications vary; providing the training is expensive [ 8 ], their job function performance is limited, and treatment and medication errors are common in healthcare practice [ 9 , 10 ]. Therefore, clinical guidelines are critical to solving such kinds of problems. However, it is manual (paper-based) and vigorously promoted as a means to improve the effectiveness of the healthcare system, patient outcomes, and healthcare costs [ 11 ]. It needs huge physical space for storage, is exposed to fire and easily lost, and is inaccessible to health professionals [ 12 ]. The manuals are poorly designed, present incomplete explanations that are difficult to read, have comprehension levels beyond the user’s capabilities, lack explicit workflow, and increase the user’s workload [ 13 , 14 , 15 ]. Moreover, the clinical guidelines are available in voluminous text files and are very laborious and time-consuming to access [ 16 ]. Therefore, this may promote distorted health information so that health professionals cannot access appropriate guidelines at the point of patient care [ 17 ].

Currently, technology has become commonplace in a healthcare setting, and there has been rapid growth in the development of medical application software [ 18 , 19 , 20 ]. Several platforms are available to assist health professionals, such as patient information management and access, communication, and consulting [ 21 , 22 ], reference and information gathering, distance medical education and training, and clinical support systems for accurate decision-making [ 23 , 24 ]. Mobile devices and mobile health applications are also among the fastest and most convenient ways for health professionals to access educational materials, including medication information, electronic clinical guidelines, and books [ 25 , 26 ].

In Sweden, a variety of wireless technologies such as mobile computing, wireless networks, and global positioning systems have been applied to ambulance care [ 27 ], and these are also functional for emergency patient care in the Netherlands [ 28 ]. In Finland, an authorized and secured mobile healthcare services system was tested in 2003 and is available nationwide, that is used for consultation, electronic prescription, and easy access to health information via mobile devices [ 29 ]. Though information technologies are an essential tool that fosters and promotes progress in healthcare and drastically reforms healthcare practices, the healthcare system in low-income countries is recognized as having lagged behind other industries in the use and adoption of information communication technologies [ 30 , 31 ]. Therefore, mobile-based clinical guidelines applications are used as job aid tools for real-time information and knowledge access and update, improving health professionals’ performance by directing and guiding in an interactive and structured manner using mobile devices [ 32 , 33 ].

In low-income countries, mobile devices are not widely utilized for daily healthcare practice in terms of providing real-time access to clinical guidelines for healthcare practitioners. Mobile-based clinical guidelines add valuable functions for health professionals in terms of presenting completed information and reducing their workload. However, healthcare professionals did not adequately use mobile devices and related applications for healthcare systems. The development of mobile-based medical applications and technology-based healthcare practices is still in its premature stages [ 34 ]. Information and communication technologies (ICT) are efficient and effective in many industries. However, they are not yet fully implemented and integrated into existing patient care systems, and healthcare institutions, particularly professionals are noticeably lagging in accepting and adopting technologies [ 35 ].

The lack of acceptance due to a lack of awareness towards mobile-based clinical guideline application, a lack of system user self-efficacy, a lack of outcome expectations, health professionals’ attitudes and perceptions [ 36 , 37 ], lack of commitment and motivation [ 34 , 38 ], lack of organizational support, the constructs of the technology acceptance model (TAM) [ 34 , 38 ], and socioeconomic characteristics of the health professionals [ 39 ] are factors for acceptance and utilization of mobile-based clinical guidelines applications in the healthcare practice. So, understanding why healthcare professionals could not accept and use mobile-based healthcare systems would accelerate hospital competition and enhance the acceptance and utilization of mobile devices and the Internet in healthcare practices [ 27 , 40 ]. It is also important to provide critical insight for the development of effective strategies to increase the efficiency and effectiveness of healthcare personnel [ 41 , 42 ].

In Ethiopia, several eHealth technologies that could support healthcare practices have been introduced. Electronic medical record system, district health information system version 2 (DHIS2), routine health information system [ 43 , 44 ], interactive voice response system, patient appointment reminder system, electronic community-based health information system, and international classification of disease version 10 (ICD-10) for disease coding and classification are mainly introduced in Ethiopia to support the healthcare system process, enhance documentation and reporting system [ 45 , 46 ]. The implementation process of the systems is extremely costly and uncertain. As a result, eHealth technology adoption and dissemination in Ethiopia are still in their infancy [ 39 , 47 , 48 ]. So, there is a high demand for an easily accessible electronic system for daily healthcare practice and challenges to patient care [ 47 ]. Therefore, before starting the mobile-based clinical guideline implementation process, creating a clear understanding of the gap that exists between the manual, and the benefits of mobile-based clinical guidelines would create awareness for system users. This would also provide an effective and efficient system development process that could make the practitioners agree and be willing to accept mobile-based clinical guidelines [ 49 ].

According to our literature searching skills and the information we have, there are no adequate studies about health professionals’ acceptance of mobile-based clinical guidelines in Ethiopia. Therefore, this study would have implications for policy design, facilitating dissemination updating clinical guidelines, receiving users’ feedback, and enhancing the clinical guideline standards. This study is critically significant for health professionals’ theoretical learning, enhancing understanding that mobile-based clinical guidelines application would help them access previous work experience, and patient history to provide accurate and consistent patient care practice.

Hence, health policy implementers and practitioners were informed that medical errors could be reduced, the accuracy of patient care could be ensured, and health professionals could be easily supported by the hand-held clinical guideline application. The study would serve as a framework for further similar research. Therefore, this study aimed to assess health professionals’ acceptance of mobile-based clinical guideline applications and test a unified theory of acceptance and technology utilization (UTAUT) model.

Theoretical background and hypothesis development

In the last decade, numerous theoretical models have been projected to assess and explain the end-user’s acceptance of information and communication technology (ICT) [ 50 ]. A unified theory of acceptance and use of technology (UTAUT) is one of the known theoretical models that is extensively used and practically tested on a wide range of ICT applications according to the end-users viewpoint [ 51 ]. UTAUT is a combination of activity theory and technology acceptance models (TAM) and has been constructed as a framework to study end-users acceptance and use of new ICT applications [ 52 ]. The UTAUT model proposed that the actual acceptance and use of technology are affected by end-users behavioural intentions (BI) [ 53 ]. The UTAUT model is an extension of other models and therefore has a strong ability to explain the acceptance and use of technology as compared with other single models [ 54 , 55 ]. The UTAUT model consists of four key construct elements that directly affect the users’ BI of acceptance of mobile-based clinical guideline applications: performance expectancy, effort expectancy, social influence, and facilitating conditions [ 51 , 56 ]. BI is additionally affected by individuals’ attitudes toward acceptance and use of new ICT applications, which are directly affected by the four key constructs [ 39 ]. Age, sex, and experience were used as moderator factors in this study. Various information communication technologies, mobile-based information systems, and integrated components that would test the health professional’s behavioural intention toward acceptance of mobile-based clinical guidelines were considered for the articulation of the study. The modified UTAUT model was applied to test the user’s acceptance, and intention to use various technologies for healthcare practice in low-income countries. For instance, a study conducted in Burundi states that the UTAUT model is critical to explaining users’ intention to adopt mobile-based information systems [ 57 ]. In Tanzania, the UTAUT model is used to test accredited drug dispensing outlet programs and to identify factors that would impact system users [ 58 ]. In Ethiopia, various studies confirmed that the modified UTAUT model is suitable for the acceptance of electronic medical and personal health record systems among the health professionals perspective [ 59 , 60 ], the adoption of e-learning [ 61 ], and the sustainable adoption of the eHealth system [ 39 ]. Moderators such as age [ 62 , 63 ], sex [ 64 , 65 , 66 ], and experience could influence the model predictors and health professionals’ intention to accept mobile-based clinical guideline applications. The practical utilization of mobile-based clinical guideline applications in resource-limited settings has not been initiated and implemented in Ethiopia. Therefore, actual system use was not measured, and the experience was removed from the structural equation model analysis as the study participants had no familiarity with mobile-based clinical guidelines application. The actual modified UTAUT model framework of the study is presented in Fig.  1 .

Modified theoretical acceptance and use of technology model

Based on the above actual UTAUT model, the following hypotheses were developed.

Performance expectancy

Performance expectance ( PE ) is the degree to which individuals believe that using ICT applications has the benefit of enhancing one’s job performance [ 67 ]. PE is identified as a strong determinant of BI’s use of ICT applications in different settings [ 67 , 68 , 69 ]. Many studies have proven that using mobile-based applications in healthcare practice has benefits for one’s health and enhances health practitioners’ job performance [ 70 , 71 , 72 ]. Performance expectance is one of the possible predictors for mHealth adoption in Burundi [ 57 ]. However, a study in Australia confirmed that performance expectance does not affect individuals’ intention to use cloud-based mHealth services [ 73 ]. Accordingly, the following hypothesis was developed.

PE has positive effects on health professionals’ attitudes toward mobile-based clinical guideline applications.

PE has a positive effect on health professionals’ BI of mobile-based clinical guideline application acceptance.

Effort expectancy

Effort expectancy (EE) is one of the crucial elements of technology acceptance in the UTAUT model and it answers “How much the new ICT technology is easy to use?” [ 56 ]. Studies depicted that EE influences users BI to accept and use new ICT applications, and it does not require efforts to work through new technology [ 39 , 74 , 75 ]. A study in a low-resource setting shows that effort expectancy is a key determinant of health professionals’ intention toward telemedicine [ 76 ]. Another study in Canada shows that information systems and technology acceptance and use are significantly influenced by effort expectancy [ 77 ]. Therefore, the following hypothesis was developed.

EE has significant values on health professionals’ attitudes toward mobile-based clinical guideline applications.

EE has significant effects on health professionals’ BI to accept mobile-based clinical guideline applications.

Social influence

Social influence ( SI ) is the degree to which system users assume that others would encourage them to use the new ICT technology [ 56 ]. According to studies, SI has a positive association with BI to accept and use new mobile health applications for healthcare practice [ 78 , 79 ]. Accordingly, the following hypothesis was formulated.

SI has significant effects on health professionals’ attitudes toward mobile-based clinical guideline applications.

SI has significant effects on health professionals’ BI to accept mobile-based clinical guideline applications.

Facilitating conditions

Facilitating conditions (FC) is one of the constructor elements in the UTAUT model [ 56 ]. It is a belief that whether there is the availability of ICT, technical infrastructure, and trustworthy support in the organization for system users [ 56 , 80 ]. FC provides system users with a sense of psychological control that in turn, influences their willingness to adopt a particular behavior. Hence, mobile-based clinical gaudiness-receiving users are required to have specific basic skills such as how to operate and use mobile phones, and how users react to the basic function of a mobile device (phone calls, sending and receiving text messages) [ 81 , 82 ]. If system users do not have these required operational skills and basic mobile functions, they will not accept and adopt mobile-based clinical guidelines applications. So, the following hypothesis was developed.

FC positively affects health professionals’ attitudes toward mobile-based clinical guideline applications.

FC positively influences the health professionals’ acceptance of mobile-based clinical guideline applications.

Computer literacy

Computer literacy (CL) is health professionals’ basic information communication technology skill and knowledge, the ability they have, and how system users are technically good at using mobile-based clinical guideline applications [ 60 , 83 ]. An individual also can seek, evaluate, and communicate information using media across a range of digital platforms, and influence acceptance of mobile-based clinical guidelines applications [ 59 , 84 , 85 ].

CL has a positive effect on health professionals’ attitudes toward mobile-based clinical guideline applications.

CL has a positive effect on health professionals’ acceptance of mobile-based clinical guideline applications.

Attitude (ATT) is a psychological construct that shows how people think, feel, and tend to behave about an object or a phenomenon [ 86 ]. It is a predisposed state of mind regarding the importance of a new system in reducing workload, enhancing work performance, and accomplishing tasks efficiently and effectively [ 39 , 87 ]. According to studies, attitude is appropriate in studying behavioural intention to accept and use new technologies, and it he one of the fundamental constructs for the successful implementation and adoption of a new technology [ 88 , 89 , 90 ]. Therefore, health professionals’ attitudes are crucial for the acceptance of mobile-based clinical guideline applications in the study setting.

ATT directly affects the BI of health professionals’ acceptance of mobile-based clinical guideline applications.

ATT mediates the relationship between PE and health professionals’ BI towards the acceptance of mobile-based clinical guideline applications.

ATT mediates the relationship between EE and health professionals’ BI towards the acceptance of mobile-based clinical guideline applications.

ATT mediates the relationship between SI and BI of health professionals to accept mobile-based clinical guideline applications.

ATT mediates the relationship between FC and BI of health professionals to accept mobile-based clinical guideline applications.

ATT mediates the relationship between CL and BI of health professionals to accept mobile-based clinical guideline applications.

The effects of moderators (age, and sex)

Studies show in China that age has significant moderating effects on effort expectancy and behavioural intention to use health technology [ 62 ], home telehealth acceptance [ 69 ], and mobile health services adoption [ 63 ]. Other studies show that age has a moderating effect on performance and effort expectancy, social influence, and behavioural intention to use health information communication technology, smart equipment, and wearable devices [ 91 , 92 ]. Similarly, sex has moderating effects on the modified UTAUT model’s construct elements [ 69 , 93 ]. For instance, being female has a significant influence on the performance expectancy of behavioural intention to use wearable technology [ 93 ]. Therefore, the following hypotheses for moderators (age and sex) have been formulated.

The effects of performance expectancy on health professionals’ intention to accept mobile-based clinical guideline applications has moderated by age.

The effects of effort expectancy on health professional intention to accept mobile-based clinical guideline application has moderated by age.

The effects of social influence on health professionals’ intention to accept mobile-based clinical guideline applications has moderated by age.

The effects of facilitating conditions on health professional intention to accept mobile-based clinical guideline application moderated by age.

The effects of computer literacy on health professionals’ intention to accept mobile-based clinical guideline applications has moderated by age.

The effects of performance expectancy on health professional intention to accept mobile-based clinical guideline application has moderated by sex.

The effects of effort expectancy on health professional intention to accept mobile-based clinical guideline application has moderated by sex.

The effects of social influence on health professional intention to accept mobile-based clinical guideline application has moderated by sex.

The effects of facilitating conditions on health professional intention to accept mobile-based clinical guideline application moderated by sex.

The effects of computer literacy on health professionals’ intention to accept mobile-based clinical guideline applications have been moderated by sex.

Study design

The institutional-based cross-sectional study design was employed among health professionals.

Study setting and period

The study was done among health professionals working in the Ilu Aba Bora Zone of the Oromia regional state, from July 04 to August 19, 2022. Ilu Aba Bora Zone is found in Southwest Ethiopia. The zone is located 600 km away from Addis Ababa, the capital city of Ethiopia. The public health facilities provide different health services for more than a million of the population in southwest parts of Ethiopia.

Study population and eligibility criteria

All healthcare professionals working in the public health facilities of the study area were the source population. All the healthcare professionals who were permanently employed were the study population. Healthcare professionals who were not present during the data collection period, who had a serious health problem, and on annual leave were excluded.

Sample size determination and sampling procedures

The sample size was determined based on structural equation model parameter criteria which were considered the number of all variance of the independent variable, covariance of exogenous variables, direct and indirect regression coefficients between latent variables, and coefficient between latent and loading of the items. Accordingly, we estimated 33, 10, 16, and 14 free parameters in the hypothetical model respectively. Consequently, a total of 73 free parameters were determined in the model. In structural equation model analysis, a minimum of 10 sample sizes were required for the single free parameters [ 94 , 95 ]. Hence, 730 sample sizes were required, and considering 10% of the non-response rate, a total of 803 sample sizes were estimated. A stratified simple random sampling method was used. Once the sample was stratified based on the types of facility, the sample was allocated in each stratum proportionally. Then, a simple random sampling technique was used to select the study subjects in each public health facility.

Data collection and quality management

A pretested self-administered tool was used. The tool of the study was adapted in reviewing previously similar studies [ 39 , 75 , 96 ]. The tool had two parts: the first part contains sociodemographic characteristics of the study participants, and the second part contains key constructs of individuals’ behavioral intention of acceptance of technology in the UTAUT model [ 67 ]. The questionnaire was constructed to test the formulated hypothesis. As shown in SI 1, a total of 26 items of questions were used for the second part. Of these questions, 4 items were for “performance expectancy”, 4 items were for “effort expectancy”, 4 items were for “facilitating condition”, 4 items were for “computer literacy”, 4 items were for “attitude”, 3 items were for “social influence”, and 3 items were for “BI of acceptance”. All the items used to measure the key construct of BI were measured by using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Two-day intensive training was delivered for the data collectors and supervisors. A pre-test was done outside of the study area (Buno Bedele Zone of Oromia region) with 10% of the total estimated sample units to check the readability and consistency of the tool. The data obtained from the pre-test was used to check the validity and reliability of the tool. Also, during the pertest health professionals’ experience of using mobile-based clinical guidelines was assessed. As a result, the study participants had no experience using mobile-based clinical guideline applications.

Operationalization

Mobile-based clinical guideline applications.

In this study, clinical guidelines are considered any clinical statements, guidelines, producers, and handbooks developed by governmental and nongovernmental agents and experts for assisting healthcare practitioners in making consistent and accurate evidence-based decisions. Therefore, properly handling these clinical guidelines using easily accessible mobile-based applications with a good format for accessibility and readability of clinical guidelines efficiently and effectively regardless of the health professional’s location [ 97 , 98 ].

Health professionals

In this study, health professionals include certified health practitioners from known governmental and private institutions who are concerned with diagnosing, treating, and preventing human illness, injury, and other physical, social, and mental health issues by the needs of the populations they serve through the standard principles and procedures [ 99 ].

Data processing and analysis

A statistical analysis technique based on the Structural Equation Model (SEM) was used to test and validate the formulated hypothesis. The data from the questionnaire were exported into SPSS software version 25. Amos version 26 software was used to analyze the data. Descriptive statistics of the study participants were calculated and presented with frequency and percentage Composite reliability was used to assess the internal reliability of each item of the constructs. The acceptable value of composite reliability (0.6) was considered for the internal reliability test [ 100 , 101 ]. Convergent validity was assessed using an Average Variance Extracted (AVE) and factor loading. Hence, AVE for each associated construct should exceed 0.50, and the items loading above 0.6 [ 102 , 103 ]. The discriminant validity was assessed using the Fornell Larcker criterion which is the square root of the AVE and cross-loading matrix. The square root of the AVE in the diagonal elements must be greater than the entire corresponding columns and rows to satisfy the discriminant validity [ 104 ]. To investigate the relationship between associated constructs, path coefficient (beta coefficients), 95% Confidence Interval, and p-value were used to check the hypothesis.

For moderator testing, the two model such as unconstrained, and constrained models were used. For both models, the moderator (age, sex) is assessed whether the moderator had an effect or significant difference for a given variable to influence the constructs and outcome variables. Accordingly, if a significant difference between the two models exists with p-value < 0.05. Then, the moderator confirmed that it had a significant effect on influencing other construct variables on the health professional’s intention to accept mobile-based clinical guidelines application.

Socio-demographic characteristics of the study participants

A total of 769 health professionals participated in this study, and returned the questionnaire, with a 95.8% response rate. From the total of 769 respondents, around one-half (52%) of the respondents were males, and the majority (63%) of the respondents were degree and diploma holders. More than half of the respondents (55.7%) were less than 30 years of age, and the majority (62%) of the health professionals had up to ten years of work experience. Five out of eleven study participants (45.30%) had a monthly salary of < = 600 birrs (Table  1 ).

Descriptive results of the constructs of the modified UTAUT model

In this study, 46.9%, 53.3%, and 61.1% of health professionals strongly agreed and intended to learn, use, and plan to use their smartphones for mobile-based clinical guidelines applications, respectively. According to the participants’ computer literacy, 32.0%, 25.6%, and 27.0% of health professionals strongly disagree on properly searching information from the online database, correcting and fixing problems happening on their computers and smartphones, and downloading and installing applications, respectively. However, 31.9% of the participants strongly disagree that they would lack the skills to practice and use the basic functions of computers and smartphones they have. According to participants’ attitudes, 46.2%, 48.5%, 45.5%, and 49.5% of participants agreed that mobile-based clinical guideline applications would be important to access the right information, useful for quality, and consistency of patient care, and they would not hesitate and fear to use the application, respectively. According to facilitating conditions, 33.1% and 36.5% of participants strongly disagreed that they would lack adequate skills and knowledge to use the application and that the application would not be compatible with their smartphone, respectively. Also, 56.4% and 43.1% of participants strongly disagreed with the resources they have, and the supportiveness of the organization to use the application, respectively.

According to social influence, 39.8%, 42.8%, and 37.3% of the participants strongly agreed that people’s influence, motivation, and options would be important to use mobile-based clinical guideline applications, respectively. According to effort expectancy, 49%, 38.8%, 54.7%, and 43.3% of the study participants strongly agree that mobile-based clinical guideline applications would be easy to use, not difficult, clear, and understandable, and would allow the practitioners to become skilful, respectively. According to performance expectancy, 30.9%, 42,7%, 43.6%, and 31.7% of the participants agreed that mobile-based clinical guideline applications would be useful to use, enable them to share information and update themselves, supportive for accurate and consistent patient care, and it wound to ensure the quality of patient care with low waiting time, respectively ( SI 2 ).

Measurement model

The convergent validity of the structural model assessment is presented in Table  2 . Based on the results, the internal consistency of each item of the latent variable was assessed by composite reliability. Composite reliability is acceptable and considered good if it ranges between 0.60 and 0.90 [ 104 , 105 ]. As a result, values of composite reliability of the latent variables ranged from a minimum of 0.750 to a maximum of 0.890, and this indicated that the respondents’ answers for each item of the latent variable were consistent and had strong internal reliability. Factor loading values of each latent variable range from a minimum of 0.63 to a maximum of 0.96. This showed that each latent variable was greater than a minimum acceptable value (0.6). The degree of variation of each latent variable was measured by the average variance extracted (AVE) value. Consequently, the analysis values of AVE ranged from a minimum of 0.582 to a maximum of 0.778. Hence, each latent variable has an estimated strong power variation between them. Consequently, the conditions for convergent validity were satisfied in this study. Furthermore, the factor loading of each item was significant on its respective construct (p-value < 0.001).

The results of discriminant validity or divergent validity between different constructs are presented in Table  3 . The elements in the matrix diagonals represent the square roots of the AVEs and are greater than the values in their corresponding row and column. As a result, all constructs in this study supported the discriminant validity of the data (Table  3 ).

Model goodness of fit

The model goodness of fit the data was checked using Chi-squire (P-value < 0.05), goodness of fit indices (GFI > 0.9), adjusted goodness of fit indices (AGFI > 0.8), normal fit indices (NFI > 0.95), Tucker–Lewis index (TLI > 0.9), comparative fit indices (CFI > 0.95), root mean square of standardized residual (RMSSR < 0.08), and (RMR < 0.08) model fit indices assessment criteria [ 86 , 106 ]. To say that the model goodness of fit is achieved, the value of Chi-squire, GFI, AGFI, TLI, RMSEA, and RMR should fulfil the cut-off point. As a result, all the required criteria were achieved and the data fitted the goodness of the model (Table  4 ).

The structural model analysis

As shown in Table  5 , the analysis report of the structural model showed that performance expectancy, facilitating condition, and computer literacy did not have any positive effects on health professionals’ attitudes toward mobile-based clinical guideline applications. Plus, facilitating conditions and computer literacy had not had any positive effects on health professionals’ BI toward acceptance of mobile-based clinical guideline applications. Effort expectancy and social influence had a significant effect on health professionals’ attitude toward mobile-based clinical guideline application with path coefficient (B-coefficient) of (β = 0.61, P-value < 0.01), and (β = 0.510, P-value < 0.01) respectively. Performance expectancy, facilitating condition, and attitude had a significant effect on health professionals’ BI of mobile-based clinical guideline application acceptance with path coefficient (B-coefficient) of (β = 0.37, P-value < 0.001), (β = 0.44, P-value < 0.001) and (β = 0.57, P-value < 0.05) respectively. All the latent variables such as performance expectancy, effort expectancy, social influence, facilitating condition, and computer literacy accounted for 57% of health professionals’ attitudes toward mobile-based clinical guideline application. All the latent variables such as performance expectancy, effort expectancy, social influence, facilitating condition, and computer literacy including health professionals’ attitude accounted for 63% of health professionals’ BI of mobile-based clinical guideline application acceptance (Fig.  2 ).

Results of the structurally modified UTAUT model. *, **, and *** indicates significant at P-value < 0.05, 0.01, and 0.001, respectively. PE : Performance expectancy, EE : Effort expectancy, SI : Social influence, FC : Facilitating conditions, ATT : Attitudes, CL : Computer literacy, BI : Behavioral intention

Mediation analysis

In the mediation analysis shown in Table  6 , the relationship between effort expectancy, and health professionals’ acceptance of mobile-based clinical guideline application had a significant partial mediation with attitude. In addition, the relationship between social influence, and health professionals’ acceptance of mobile-based clinical guideline applications had a significant partial mediation with attitude. Accordingly, effort expectancy and social influence had an indirect effect relationship with health professionals’ BI towards mobile-based clinical guidelines application acceptance with standardized estimation coefficient ( β = 0.22, P-value = 0.027 ), and ( β = 0.19, P-value = 0.031 ), respectively.

Moderating effects of sex and age of health professionals on intention to accept mobile-based clinical guideline application

The effects of sex, and age on the relationship between performance expectancy, effort expectancy, social influence, facilitating conditions, and computer literacy with health professionals’ intention to accept mobile-based clinical guideline applications was investigated. The moderators were estimated both in constrained and unconstrained models.

Accordingly, performance expectancy, facilitating conditions, and social influence on health professionals’ intention to accept mobile-based clinical guideline applications had not significantly moderated by the sex of health professionals. However, computer literacy and effort expectancy on health professionals’ intention to accept mobile-based clinical guideline applications was significantly moderated by sex. Being male had a significant effect on the effort expectancy of health professionals’ intention to accept mobile-based clinical guideline applications with a path coefficient of 0.712 and a p-value of 0.018. Being female also had a significant effect on the computer literacy of health professionals’ intention to accept mobile-based clinical guideline applications with a path coefficient of 0.316 and a p-value of 0.001 (Table  7 ). Therefore, H23 and H26 were supported in this study.

For measuring the effects of age on the constructs, average age [ 36 ] was used as a cut-off point to dichotomize age as young (< 36 years) and old (≥ 36 years). Therefore, age had a significant effect on the computer literacy of health professionals’ intention to accept mobile-based clinical guideline applications, where young health professionals positively influenced health professionals’ acceptance of mobile-based clinical guideline applications with a path coefficient of 0.718, and a p-value of 0.031(Table  8 ). Therefore, H21 was supported.

This study was conducted to determine the effects of constructs of the UTAUT model on health professionals’ acceptance of mobile-based clinical guideline applications before the actual use of the applications. In this study total of 803 health professionals participated. Therefore, the study was different from other similar studies in terms of the representative sample size used, which is important to save resources to make decisions based on this study. In addition, the study verified that the constructs (PE, EE, SI, FC, CL, and ATT) of the UTAUT model would explain individuals’ attitudes towards mobile-based clinical guidelines application and health professionals’ acceptance before the actual use of the application. In this study, convergent and divergent validity were assessed, and the model goodness of fit was also tested. As a result, all the mentioned criteria of the structural equation model were achieved.

A hypothesis for all the constructs was formulated, and their effects on the health professionals’ acceptance of mobile-based clinical guidelines applications were checked. As a result, performance expectancy, facilitating conditions, and computer literacy had no positive effects on health professionals’ attitudes toward mobile-based clinical guidelines application ( H1 , H7 , and H9 ). Additionally, facilitating conditions and computer literacy had no positive effects on health professionals’ acceptance of mobile-based clinical guidelines ( H8 and H10 ). Performance expectancy and effort expectancy had a significant effect on health professionals’ behavioral intentions, and attitudes toward mobile-based clinical guideline applications, respectively (H2 and H3). Plus, facilitating conditions and social influence had a significant effect on health professionals’ Behavioral intentions, and attitudes towards mobile-based clinical guideline application acceptance, respectively ( H8 and H5 ). According to hypothesis H11 , health professionals’ attitudes had a direct effect on their Behavioral intentions toward the mobile-based clinical guidelines application. In the mediation analysis result, effort expectancy and social influence had a significant indirect and standardized partial relationship with health professionals’ acceptance of mobile-based clinical guidelines applications.

Effort expectancy had a significant effect on health professionals’ attitudes towards mobile-based clinical guideline applications, and its relationship with health professionals’ acceptance of mobile-based clinical guideline applications was mediated by the health professionals’ attitudes. This finding was supported by similar studies conducted in different geographical areas [ 107 , 108 ]. Other studies also proved that effort expectancy had a significant influence on the adoption of healthcare information technology, and MHealth applications [ 71 , 108 , 109 ]. The finding opposes a study report that states mobile applications are difficult to use, the benefits of using mobile applications are offset by the effort to use the mobile application, as well as the more complex an innovation is, the lower its rate of acceptance, and adoption of the mobile-based clinical guideline application again [ 110 , 111 ]. However, effort expectancy has a positive influence on individuals’ acceptance of new technology (mobile-based clinical guideline application), and its indirect effect on attitude [ 112 ]. This might be due to health professionals’ attitudes, the belief that using the new application is easy, and the intention to use mobile-based clinical guideline applications positively influenced by the effort made to use mobile applications [ 39 ]. Plus, effort expectancy is associated with diagnosis and medication error reduction [ 113 ], applications’ flexibility, friendliness, familiarity, and its easiness of individuals to use. Additionally, mobile phones are now routinely used in education, entertainment, communication, and healthcare facilities [ 67 ]. So, it might not need too much effort, and users might not face technical problems.

The social influence had a significant effect on health professionals’ attitudes toward mobile-based clinical guideline applications, and its relationship with health professionals’ acceptance of mobile-based clinical guideline applications was mediated by the health professionals’ attitudes. This was congruent with other similar studies [ 60 , 75 , 86 , 114 ]. It was concluded that the viewpoints and opinions of others regarding the use of information technology in education and learning were affected by health professionals’ behavioral intentions for the frequent and daily use of technology [ 115 ]. This is associated with expert clinical guideline development skills for disease management and might influence individual health professionals’ acceptance of mobile-based clinical guideline applications [ 116 ].

Performance expectancy had a significant effect on health professionals’ acceptance of mobile-based clinical guideline applications. This could be because mobile-based clinical guidelines applications could be useful for assisting health professionals in monitoring the disease progression of the patient and managing disease [ 117 ]. Additionally, mobile clinical guidelines applications could also provide health professionals with real-time information on the patient’s specific health condition [ 118 , 119 ]. So, mobile-based clinical guidelines could be effective for better healthcare outcomes. Performance expectancy enhances the productivity of health professionals and is efficient for the time spent in operation, patient management, and the care provider’s intention and attitude toward mobile-based clinical guideline application acceptance [ 39 ]. This study’s findings were similar to those of previous studies [ 72 , 120 , 121 ].

The facilitating conditions had a significant effect on health professionals’ BI of mobile-based clinical guideline application acceptance. This finding was consistent with similar studies conducted in Ethiopia [ 60 , 86 ], Nigeria [ 122 ], South Africa [ 123 ], and Malaysia [ 124 ]. Facilitating conditions such as organizational setting, preliminary skill, and knowledge they had on a mobile device, resources, and availability of training for information sharing [ 122 ], and system quality might have an important role in predicting users’ actual acceptance of mobile-based clinical guideline applications [ 86 ]. All these facilitating conditions might be user-friendly, comprehensive, and easily available for mobile-based clinical guidelines application acceptance by individuals.

Attitude had a significant effect on health professionals’ acceptance of mobile-based clinical guideline applications. This finding was consistent with previous studies [ 39 , 86 ]. This might be because health professionals’ attitudes toward using mobile-based systems have improved over time, and individuals’ sociodemographic characteristics and educational level affect their attitudes which further affect their behavioral intention of technology acceptance [ 125 , 126 ].

Conclusions and recommendations

This study reported that the unified theory of acceptance and use of technology (UTAUT) model proved a suitable model to assess health professionals’ attitudes and behavioral intentions towards the acceptance of mobile-based clinical guidelines applications. Social influence, effort expectancy, and facilitating conditions were significant constructs for health professionals’ acceptance of mobile-based clinical guideline applications. Health professionals’ attitude toward mobile-based clinical guideline application was another strong construct in the UTAUT model for the acceptance of mobile-based clinical guidelines. Plus, effort expectancy and social influence had a positive effect on health professionals’ attitudes toward mobile-based clinical guideline applications. The development of user-friendly mobile-based clinical guideline applications, based on user’s requirements and in line with national standards of clinical guidelines, would be encouraged for consistent and accurate health professionals’ decision-making processes. So, stakeholders and policymakers are advised to build the capacity and technical skills of health professionals to enhance their overall computer literacy. Moreover, resources and organizational support of health professionals would be critical for the acceptance of mobile-based clinical guideline applications.

Implications of the study and future research directions

Theoretical implications.

This study contributes to the growing body of literature on the application of mobile devices for healthcare practice and education promotion. The applied extended UTAUT model was proven to be suitable for predicting mobile-based clinical guideline acceptance. This study assessed the acceptance of mobile-based clinical guideline applications among health professionals’ perspectives, which aided in the development and enhancement of locally relevant clinical practice guidelines. This study may alleviate any concerns of readers about the UTAUT model, and mobile-based clinical guidelines, and it serves as a baseline for researchers since there is insufficient evidence on a similar topic.

Practical implications

This study provides valuable implications for fostering the future implementation of mobile-based clinical guidelines. Based on the significant predictors, the current study may be important to offer tailored programs to increase users’ digital knowledge and to ensure that using mobile-based clinical guidelines applications is easy and simple. Performance expectancy is a significant predictor of the acceptance of mobile-based clinical guidelines. This indicates that it is vital to demonstrate the advantages of mobile-based clinical guidelines to healthcare professionals.

Implications for future research direction

Future research should therefore concentrate on approaches to simplifying the acceptance level of mobile-based clinical guidelines, and removing technical barriers. Future research should focus on exploring further suitable and specific predictors to enhance the viability of the UTAUT model in a health-related context. The proposed predictors could also easily be applied in studies on the actual use of locally available mobile-based systems in healthcare practice that enable researchers to examine their ultimate predictive power. Researchers are also encouraged to conduct similar studies on governmental and non-governmental health institutions. Decision makers, care healthcare providers, and system developers could use this study’s findings to increase the adoption of mobile-based clinical guidelines in the future.

Strengths and limitations of the study

This study will provide input for future research and mobile-based clinical guidelines application implementation and adoption in low-income settings. Additionally, this study proved that constructs in the UTAUT model affect health professionals’ intention to accept new technology. Since the study is cross-sectional, there might be a temporal relationship between the effects of constructs and individuals’ behavioral intentions to accept mobile-based clinical guidelines applications. This study did not attempt to control the impact of confounding variables on the health professionals’ intention to accept mobile-based clinical guideline applications.

Data availability

All the data generated, and analyzed during the study are included in this article.

Abbreviations

Behavioural intention

Information communication technology

Technology acceptance model

Unified theory of acceptance and technology use

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We would like to express our deepest appreciation to data collectors and supervisors.

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AWD was involved in topic selection, study design, data management, data analysis, interpretation, and discussion of the findings. AWD, MHK, and ADW had a role in manuscript preparation, and reviewing the work. All authors had read and approved the final manuscript for submission.

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Ethics approval was obtained from the Institutional Review Committee of the Health Science College of Mattu University with reference number ARc/2012/14 . Informed verbal consent from the study participants was obtained before data collection. There is no clinical trial number for this study as long as the study is not a clinical trial. Moreover, the study was performed following the declaration of Helsinki.

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Demsash, A.W., Kalayou, M.H. & Walle, A.D. Health professionals’ acceptance of mobile-based clinical guideline application in a resource-limited setting: using a modified UTAUT model. BMC Med Educ 24 , 689 (2024). https://doi.org/10.1186/s12909-024-05680-z

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Received : 27 November 2022

Accepted : 19 June 2024

Published : 25 June 2024

DOI : https://doi.org/10.1186/s12909-024-05680-z

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