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What it is and how it's used in sociology
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.
Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.
In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.
Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true.
A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.
Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.
Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.
Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.
Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.
Updated by Nicki Lisa Cole, Ph.D
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Developing a Research Question
When researchers do not have predictions about what they will find, they conduct research to answer a question or questions, with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses. A hypothesis is a statement, sometimes but not always causal, describing a researcher’s expectations regarding anticipated finding. Often hypotheses are written to describe the expected relationship between two variables (though this is not a requirement). To develop a hypothesis, one needs to understand the differences between independent and dependent variables and between units of observation and units of analysis. Hypotheses are typically drawn from theories and usually describe how an independent variable is expected to affect some dependent variable or variables. Researchers following a deductive approach to their research will hypothesize about what they expect to find based on the theory or theories that frame their study. If the theory accurately reflects the phenomenon it is designed to explain, then the researcher’s hypotheses about what would be observed in the real world should bear out.
Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and legalization of marijuana. Perhaps you have done some reading in your spare time, or in another course you have taken. Based on the theories you have read, you hypothesize that “age is negatively related to support for marijuana legalization.” What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their support for marijuana legalization decreases. Thus, as age moves in one direction (up), support for marijuana legalization moves in another direction (down). If writing hypotheses feels tricky, it is sometimes helpful to draw them out. and depict each of the two hypotheses we have just discussed.
Note that you will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a relationship has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not bear out. Instead, researchers tend to say that their hypotheses have been supported (or not) . This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining a relationship will be discovered. Researchers may also discuss a null hypothesis, one that predicts no relationship between the variables being studied. If a researcher rejects the null hypothesis, he or she is saying that the variables in question are somehow related to one another.
Quantitative and qualitative researchers tend to take different approaches when it comes to hypotheses. In quantitative research, the goal often is to empirically test hypotheses generated from theory. With a qualitative approach, on the other hand, a researcher may begin with some vague expectations about what he or she will find, but the aim is not to test one’s expectations against some empirical observations. Instead, theory development or construction is the goal. Qualitative researchers may develop theories from which hypotheses can be drawn and quantitative researchers may then test those hypotheses. Both types of research are crucial to understanding our social world, and both play an important role in the matter of hypothesis development and testing. In the following section, we will look at qualitative and quantitative approaches to research, as well as mixed methods.
An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Learning objectives.
Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.
Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.
A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.
Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.
But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.
Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.
The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.
Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.
As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).
When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.
To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.
There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.
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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).
A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).
A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.
Wrong hypotheses, rightly worked from, have produced more results than unguided observation
—Augustus De Morgan, 1872[ 1 ]—
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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3
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Overview of the Scientific Method
Learning objectives.
Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.
Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.
A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.
Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.
But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.
Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.
The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.
As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).
When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.
To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.
There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.
A coherent explanation or interpretation of one or more phenomena.
A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.
A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.
The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.
Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.
In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.
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Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.
The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).
A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart
Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black
Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt
A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)
From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.
The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.
Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.
Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.
Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:
Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.
The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.
A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.
A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.
For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.
For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.
For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.
While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.
It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.
A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.
If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.
This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”
If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.
Hypotheses can be derived from various sources. Some of the sources is given below:
State of knowledge, continuity of research.
Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.
Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.
This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.
An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.
Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.
The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.
Null hypothesis.
The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.
Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.
Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .
Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.
As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .
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Methodology
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | to broader populations. . | |
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Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
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Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | . . | |
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Experimental |
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Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
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.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
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Hypothesis testing is the act of testing a hypothesis or a supposition in relation to a statistical parameter. Analysts implement hypothesis testing in order to test if a hypothesis is plausible or not.
In data science and statistics , hypothesis testing is an important step as it involves the verification of an assumption that could help develop a statistical parameter. For instance, a researcher establishes a hypothesis assuming that the average of all odd numbers is an even number.
In order to find the plausibility of this hypothesis, the researcher will have to test the hypothesis using hypothesis testing methods. Unlike a hypothesis that is ‘supposed’ to stand true on the basis of little or no evidence, hypothesis testing is required to have plausible evidence in order to establish that a statistical hypothesis is true.
Perhaps this is where statistics play an important role. A number of components are involved in this process. But before understanding the process involved in hypothesis testing in research methodology, we shall first understand the types of hypotheses that are involved in the process. Let us get started!
In data sampling, different types of hypothesis are involved in finding whether the tested samples test positive for a hypothesis or not. In this segment, we shall discover the different types of hypotheses and understand the role they play in hypothesis testing.
Alternative Hypothesis (H1) or the research hypothesis states that there is a relationship between two variables (where one variable affects the other). The alternative hypothesis is the main driving force for hypothesis testing.
It implies that the two variables are related to each other and the relationship that exists between them is not due to chance or coincidence.
When the process of hypothesis testing is carried out, the alternative hypothesis is the main subject of the testing process. The analyst intends to test the alternative hypothesis and verifies its plausibility.
The Null Hypothesis (H0) aims to nullify the alternative hypothesis by implying that there exists no relation between two variables in statistics. It states that the effect of one variable on the other is solely due to chance and no empirical cause lies behind it.
The null hypothesis is established alongside the alternative hypothesis and is recognized as important as the latter. In hypothesis testing, the null hypothesis has a major role to play as it influences the testing against the alternative hypothesis.
(Must read: What is ANOVA test? )
The Non-directional hypothesis states that the relation between two variables has no direction.
Simply put, it asserts that there exists a relation between two variables, but does not recognize the direction of effect, whether variable A affects variable B or vice versa.
The Directional hypothesis, on the other hand, asserts the direction of effect of the relationship that exists between two variables.
Herein, the hypothesis clearly states that variable A affects variable B, or vice versa.
A statistical hypothesis is a hypothesis that can be verified to be plausible on the basis of statistics.
By using data sampling and statistical knowledge, one can determine the plausibility of a statistical hypothesis and find out if it stands true or not.
(Related blog: z-test vs t-test )
Now that we have understood the types of hypotheses and the role they play in hypothesis testing, let us now move on to understand the process in a better manner.
In hypothesis testing, a researcher is first required to establish two hypotheses - alternative hypothesis and null hypothesis in order to begin with the procedure.
To establish these two hypotheses, one is required to study data samples, find a plausible pattern among the samples, and pen down a statistical hypothesis that they wish to test.
A random population of samples can be drawn, to begin with hypothesis testing. Among the two hypotheses, alternative and null, only one can be verified to be true. Perhaps the presence of both hypotheses is required to make the process successful.
At the end of the hypothesis testing procedure, either of the hypotheses will be rejected and the other one will be supported. Even though one of the two hypotheses turns out to be true, no hypothesis can ever be verified 100%.
(Read also: Types of data sampling techniques )
Therefore, a hypothesis can only be supported based on the statistical samples and verified data. Here is a step-by-step guide for hypothesis testing.
First things first, one is required to establish two hypotheses - alternative and null, that will set the foundation for hypothesis testing.
These hypotheses initiate the testing process that involves the researcher working on data samples in order to either support the alternative hypothesis or the null hypothesis.
Once the hypotheses have been formulated, it is now time to generate a testing plan. A testing plan or an analysis plan involves the accumulation of data samples, determining which statistic is to be considered and laying out the sample size.
All these factors are very important while one is working on hypothesis testing.
As soon as a testing plan is ready, it is time to move on to the analysis part. Analysis of data samples involves configuring statistical values of samples, drawing them together, and deriving a pattern out of these samples.
While analyzing the data samples, a researcher needs to determine a set of things -
Significance Level - The level of significance in hypothesis testing indicates if a statistical result could have significance if the null hypothesis stands to be true.
Testing Method - The testing method involves a type of sampling-distribution and a test statistic that leads to hypothesis testing. There are a number of testing methods that can assist in the analysis of data samples.
Test statistic - Test statistic is a numerical summary of a data set that can be used to perform hypothesis testing.
P-value - The P-value interpretation is the probability of finding a sample statistic to be as extreme as the test statistic, indicating the plausibility of the null hypothesis.
The analysis of data samples leads to the inference of results that establishes whether the alternative hypothesis stands true or not. When the P-value is less than the significance level, the null hypothesis is rejected and the alternative hypothesis turns out to be plausible.
As we have already looked into different aspects of hypothesis testing, we shall now look into the different methods of hypothesis testing. All in all, there are 2 most common types of hypothesis testing methods. They are as follows -
The frequentist hypothesis or the traditional approach to hypothesis testing is a hypothesis testing method that aims on making assumptions by considering current data.
The supposed truths and assumptions are based on the current data and a set of 2 hypotheses are formulated. A very popular subtype of the frequentist approach is the Null Hypothesis Significance Testing (NHST).
The NHST approach (involving the null and alternative hypothesis) has been one of the most sought-after methods of hypothesis testing in the field of statistics ever since its inception in the mid-1950s.
A much unconventional and modern method of hypothesis testing, the Bayesian Hypothesis Testing claims to test a particular hypothesis in accordance with the past data samples, known as prior probability, and current data that lead to the plausibility of a hypothesis.
The result obtained indicates the posterior probability of the hypothesis. In this method, the researcher relies on ‘prior probability and posterior probability’ to conduct hypothesis testing on hand.
On the basis of this prior probability, the Bayesian approach tests a hypothesis to be true or false. The Bayes factor, a major component of this method, indicates the likelihood ratio among the null hypothesis and the alternative hypothesis.
The Bayes factor is the indicator of the plausibility of either of the two hypotheses that are established for hypothesis testing.
(Also read - Introduction to Bayesian Statistics )
To conclude, hypothesis testing, a way to verify the plausibility of a supposed assumption can be done through different methods - the Bayesian approach or the Frequentist approach.
Although the Bayesian approach relies on the prior probability of data samples, the frequentist approach assumes without a probability. A number of elements involved in hypothesis testing are - significance level, p-level, test statistic, and method of hypothesis testing.
(Also read: Introduction to probability distributions )
A significant way to determine whether a hypothesis stands true or not is to verify the data samples and identify the plausible hypothesis among the null hypothesis and alternative hypothesis.
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Different Types of Research Methods
Home » Research Methods – Types, Examples and Guide
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Definition:
Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.
Types of Research Methods are as follows:
Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.
The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods
Research Method | Quantitative | Qualitative | Mixed Methods |
---|---|---|---|
To measure and quantify variables | To understand the meaning and complexity of phenomena | To integrate both quantitative and qualitative approaches | |
Typically focused on testing hypotheses and determining cause and effect relationships | Typically exploratory and focused on understanding the subjective experiences and perspectives of participants | Can be either, depending on the research design | |
Usually involves standardized measures or surveys administered to large samples | Often involves in-depth interviews, observations, or analysis of texts or other forms of data | Usually involves a combination of quantitative and qualitative methods | |
Typically involves statistical analysis to identify patterns and relationships in the data | Typically involves thematic analysis or other qualitative methods to identify themes and patterns in the data | Usually involves both quantitative and qualitative analysis | |
Can provide precise, objective data that can be generalized to a larger population | Can provide rich, detailed data that can help understand complex phenomena in depth | Can combine the strengths of both quantitative and qualitative approaches | |
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A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.
Mixed Research Example:
A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.
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Research methods serve several purposes, including:
Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.
Here are some situations when research methods may be appropriate:
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Research on the impact of technology mergers and acquisitions on corporate performance: an empirical analysis based on china’s pharmaceutical industry.
There is intense competition among pharmaceutical companies with the rapid growth of the global pharmaceutical industry. In recent years, China has continuously increased the reform of the medical system. Technology mergers and acquisitions (M&A) in China’s pharmaceutical industry have emerged in this complex policy and economic background. This paper conducts an empirical study from the dual perspectives of financial performance and innovation performance, based on unbalanced panel data of Chinese listed pharmaceutical firms from 2012 to 2022. The impact of technology M&A on firm performance is analyzed in terms of the heterogeneity of firm characteristics. Meanwhile, the relationship between R&D investment in technology M&A and firm performance is examined. The results show that technology M&A can promote the performance of pharmaceutical companies, and R&D investment has a mediating effect on the impact of technology M&A on corporate performance. Based on the above findings, this study enriches the relevant literature on technology M&A in the pharmaceutical industry, provides warnings and suggestions for pharmaceutical companies to improve corporate performance through technology M&A, and provides reference materials for future policy formulation.
Competition among enterprises is intensifying in the context of global economic integration ( 1 ). Governments have intensified their scientific and technological innovation backing to gain a competitive edge. It is an important indicator that the innovation capacity of pharmaceutical companies can measure a country’s strength in science and technology innovation ( 2 ). The pharmaceutical market has opportunities and challenges when China is economically transforming and upgrading ( 3 ). Pharmaceutical companies can improve their innovation performance in two ways: internal research and development (R&D) and external mergers and acquisitions (M&A). It is relatively slow to depend on internal R&D to enhance technology because of limitations in technical resources and research and development professionals ( 4 ). Therefore, there is a growing tendency among pharmaceutical companies to pursue advanced pharmaceutical development technologies to acquire specialized technological resources from external sources and to use technology mergers and acquisitions to achieve leapfrog innovation ( 5 ).
In recent years, China has been increasing its efforts to reform the pharmaceutical system and to develop technological innovations in pharmaceutical enterprises ( 6 ). There have been mergers and acquisitions of companies in the pharmaceutical industry in China’s complex policy and economic background. It is increasingly recognized among pharmaceutical companies that they can gain access to advanced technologies and products by engaging in technological mergers and acquisitions, which allows them to establish an advantageous market position ( 7 ). However, China’s pharmaceutical industry is relatively dispersed regarding industrial structure. Although the number of pharmaceutical enterprises is large, the scale is generally small, and the innovation ability is relatively weak. There is an apparent gap between Chinese pharmaceutical companies and large multinational pharmaceutical companies in developed countries regarding research and development capabilities, innovation capabilities, financial strength, etc. ( 8 ). China has implemented many measures to promote and facilitate the technological advancement of pharmaceutical companies in recent years. As an illustration, the government will offer tax advantages, financial assistance, and other policy measures to stimulate firms to enhance their investment in research and development. These policies create a favorable external environment and conditions for Chinese pharmaceutical businesses to engage in technology mergers and acquisitions, making such mergers and acquisitions an essential means to boost the development of the pharmaceutical industry.
The earliest concept of technology mergers and acquisitions (M&A) is Williamson’s 1975 proposal that technology M&A is a mergers and acquisitions activity with the primary goal of acquiring the target’s technological resources ( 9 ). Technology M&A is a highly effective technique for companies to quickly get innovative resources and strengthen their ability to innovate technologically in response to changes in their business models ( 10 ). Mergers and acquisitions among enterprises originated in the United States and have since expanded worldwide ( 11 ). Scholars from various countries have researched technology mergers and acquisitions, with the most influential researchers appearing in Sweden and the United States. Jacobsson and Granstrand revealed in 1983 and 1984, respectively, that small firms in technology M&As have seller characteristics in deal offers in the M&A market due to their advanced technology patents. Granstrand ( 12 ) discussed the role of technology M&A in the mergers and acquisitions process using the “theory of technology-based enterprises.” Wang and Han ( 13 ) examined how the absorptive capacity of American companies might influence technological innovation as a moderator. Enterprises’ absorptive capacity enables them to incorporate and utilize external technological information as internal knowledge effectively. It is a relatively late research on technology mergers and acquisitions in China. Wu et al. ( 14 ) examined the developmental trajectory of China’s firms’ technological prowess, as demonstrated through their adoption of new technologies, expansion of production capacity, and ability to innovate. The primary purpose of leading companies engaging in M&A is to address their deficiencies in specific areas, enhance the diversified growth of their technology research and development, and lay the foundation for comprehensive technological innovation in the future. Based on existing research on technology M&A, one view is that technology mergers and acquisitions can efficiently address R&D disadvantages and enhance the knowledge capacities of acquiring organizations ( 15 ). Simultaneously, technology M&A provides exit channels other than IPOs for the founders of target firms, thus reducing their entrepreneurial risk ( 16 ). This initiative aims to incentivize target firms to enhance their investment in research and development and intensify their efforts in technological knowledge innovation ( 7 ). Furthermore, Ghosh et al. ( 8 ) propose that technology M&A offers a faster way to obtain external technological resources than internal research and development. It is an effective way for enterprises to master the technical knowledge of the target enterprise and absorb high-tech talents. Another view is that technology M&A may hinder the improvement of internal research and development capabilities if firms rely too much on external technological resources. The enterprise’s intangible resources are not effectively accumulated, and the absorption of external knowledge resources will be negatively affected ( 17 ). Szucs ( 18 ) argues it will diminish the enterprise’s ability to innovate independently if the primary objective of technological mergers and acquisitions is to evade market competition rather than efficiently incorporate and utilize the acquired technology.
To sum up, the extant literature on technological M&A and enterprise performance lacks consensus and is confined to a singular perspective. Only a few numbers of researchers have empirically investigated technological M&A in the pharmaceutical industry. Different firms are affected differently by undertaking technology M&As. It is essential to consider the diversity of innovation subjects, whose characteristics such as property rights, size, and geographical location should also not be ignored. Therefore, it is important to further investigate the effect of technology M&A on firm performance and understand the mechanisms by which technology M&A enhances firm performance. This paper empirically analyses the impact of technology M&A on firm performance using a fixed-effects approach based on unbalanced panel data of listed pharmaceutical firms in China from 2012 to 2022. In addition, it comprehensively examines the diversity among enterprises with varying ownership characteristics, sizes, and geographic locations. It then further analyses the role of R&D investment as a mediator between technology M&A and firm performance. It offers rich materials for China’s pharmaceutical industry to launch technology M&As, aiding firms in gaining a deeper understanding of technological expansion through M&A to enhance their innovation capabilities. Furthermore, this article analyzes the outcomes of the present government’s strategy to encourage technological advancement in China and offers pertinent information for future policy development using the most recent sample data.
Compared with the existing results, this research offers possible contributions as follows: Firstly, it is the inaugural empirical study that examines financial performance and innovation performance from a dual perspective. The extensive literature on company performance mostly concentrates on individual performance indicators. This paper integrates the two variables into a single variable for research and analysis to systematically evaluate the influence of technological mergers and acquisitions on the performance of pharmaceutical manufacturing companies. It complements the limited theory and empirical evidence available in this field. Secondly, this paper explores the relationship between technology M&A and firm performance and frames the study of R&D inputs to explore its mechanism as a mediating variable. Existing literature has conducted a great deal of research and studies from the perspective of the respective impact of R&D inputs and technology M&A on firm performance, and some scholars have made several studies from the relevant aspects of individual firms. This paper will provide R&D inputs into the study of technology M&A and its impact on company performance, enriching the relevant literature. Thirdly, there are fewer studies on technology M&A in China’s pharmaceutical companies. This paper specifically studies the impact of technology M&A on firm performance in the pharmaceutical industry with Chinese characteristics (heterogeneity) to provide a realistic reference for the current innovation practice of pharmaceutical enterprises.
The rest of the paper is organized as follows. In the “Literature Review and Research Hypotheses” section, this paper reviews the previous literature and puts forward the hypotheses of this paper. The “Research Design” section provides data sources, variable selection, and model setup. The “Empirical Analysis” and “Heterogeneity Analysis” sections discuss the results. Finally, “Conclusions and Recommendations, Shortcomings and Prospects” is given.
2.1 technology mergers and acquisitions and firm performance.
With the growing requirement for enterprise innovation, obtaining external technical resources has become a major incentive for companies to combine and acquire. In theory, the mechanism by which companies rely on acquisitions to enhance innovation output is mainly reflected in two aspects. One is the selection mechanism; it is more efficient for companies with poor innovation capabilities to acquire innovation by acquiring companies with substantial expertise or ready-made patents than a direct investment in independent innovation ( 19 ). Cassiman et al. ( 20 ) argues that the principal merger party will purposefully select target firms that possess their missing technological knowledge so that they can update their existing knowledge after the technology acquisition. The study conducted by Chen ( 21 ) shows that there is a very important role in the development of new ideas and the existing knowledge base of firms in enhancing innovation core competitiveness after M&A. The second is synergy; it will be enlarged to cover the knowledge stock of the leading merging company after a technology M&A. It is beneficial for enterprises with a deep stock of knowledge to absorb external technological resources, which enhances their innovation capacity and increases the firm’s innovation output ( 22 ). Zhang et al. ( 23 ) showed that technology M&A can avoid the knowledge cocoon trap and innovation path dependence generated by long-term independent research and development, which can rapidly update and expand the existing knowledge stock of enterprises. Enterprises have complementary technological resources to enhance innovation power because of the synergistic effect.
Technology mergers and acquisitions are an effective strategy for enterprises to acquire innovative resources and enhance corporate performance rapidly. According to Zhao ( 24 ), an analysis of M&A cases across various industries in the United States between 1984 and 1997 revealed that M&A transactions driven by the goal of technology innovation are a common phenomenon. It is through technology M&As that companies, especially those with a weak innovation capacity before the M&A, will increase the number of patents obtained. Entezarkheir and Moshiri ( 25 ) argues that M&A significantly positively affects corporate innovation, and heterogeneity will exist across industries. Chinese scholars have shown that technology mergers and acquisitions clearly influence firms’ innovation performance ( 26 ). Qu ( 27 ) analyzed the intrinsic link between a company’s technology M&A and innovation performance and found that complementary and substitutive technology M&As significantly boost the firm’s innovation performance. Also, the study conducted by Yang and Zhou ( 28 ) demonstrated that the impact of technical innovation resulting from technology M&As becomes more evident when the acquired firm experiences significant growth. Wu et al. ( 29 ) investigated the effect of knowledge integration on firms’ innovation performance based on different technology M&A modes. Enterprises will change their knowledge base regarding width and depth when they carry out two modes of technology M&A. Therefore, the performance of innovation in enterprises will yield varying outcomes.
Additionally, Nesta and Saviotti ( 30 ) conducted an empirical study on the pharmaceutical industry and concluded that the higher the acquired firm’s technological R&D base, the more effective it is in improving the R&D capability of the primary acquiring firm after the merger. Also, it is more conducive for firms with a solid long-term technological R&D base to enhance post-merger innovation performance. Lin and Jang ( 31 ) examined merger and acquisition data from the United States pharmaceutical industry and argued that complementarities between firms can improve technological development and innovation. Firms should find companies in the same industry as theirs that match their size and technology for strategic integration. Hao and Ren ( 32 ) studied the evolution of issues related to technology M&A in high-tech enterprises. They proposed that the impact of technology M&A on the technological integration of enterprises varies depending on the industry. Technology M&A particularly promotes R&D in the pharmaceutical industry and suggests relevant countermeasures for enterprises and authorities. Yu and Wang ( 33 ) used a double-difference method to compare the innovation performance of technology M&As between firms that executed M&As and firms that did not perform M&As throughout different policy stages. The study results showed that firms engaging in technology M&As could improve their innovation performance in the short run before implementing the policy. Still, the innovation effect was negative in the long run. After implementing the policy, firms that engage in technology M&As show adverse innovation effects in the short term. The study results provide a realistic reference for the future decision-making of enterprises and the establishment of national policies.
Based on the combination of the above theories and literature, this paper proposes the following research hypotheses:
H1 : Technology mergers and acquisitions positively affect firm performance.
There are two basic approaches to innovation for enterprises: closed innovation and open innovation. Closed innovation is mainly based on internal R&D, and R&D investment is the core bloodline of firms’ innovation activities ( 22 ), and firm performance is closely related. It can enhance the enterprise’s independent R&D capability, which starts from within the enterprise to invest enterprise resources in R&D activities. Enterprises can master the core technology and form their core competitiveness through independent R&D to occupy a favorable position in the market ( 34 ). One of the most important avenues for open innovation is technology M&A, which allows for rapid access to external technological resources and core knowledge capabilities and improves the firm’s innovation ability ( 35 ). Compared with the long time, high risk, and high investment required for independent R&D, technology M&A can more rapidly acquire the technological knowledge the target firm holds ( 4 ). Firms relying solely on internal R&D to realize innovation can increase riskiness in the context of the increasing speed of innovation iteration ( 36 ). There is a growing realization in enterprises that innovation does not only come from within the enterprise; external resource integration is also an essential part ( 37 ).
Williamson’s Transaction cost theory (9) states that external technological resource acquisition replaces internal R&D skills. Companies experience transaction costs when they acquire external technology resources, which impact the incorporation of these resources within the company. Cohen and Levinthal ( 6 ) contend that the firm’s internal R&D capacity plays a significant role in assimilating and innovating external resources. However, it will negatively impact the company’s performance due to an excessive dependence on external technology resources and a lack of logical incorporation of externally acquired technological resources. Hitt et al. ( 38 ) and Jensen ( 39 ) propose that the reduction in R&D spending by innovative companies following mergers and acquisitions diminishes their level of R&D intensity. Firms interrupt their existing development plans to use the target company’s resources better and spend a lot of time on strategic adjustments at the managerial level, slowing down technological innovation in the company. It has also been argued that inadequate integration measures are not taken after a merger or acquisition, or if there is inertia in autonomous innovation due to technology purchase, this can negatively impact a firm’s innovation performance ( 40 ). In addition, Wang and Ma ( 36 ) discovered that the R&D expenditure of the dominant party involved in a merger and acquisition has a moderating effect on the process using a multiple regression model. This moderation promotes the combination of resources and collaborative innovation. Gandal and Scotchmer ( 41 ) highlighted that corporate governance issues influence the optimal selection of R&D investment by decision-makers, which subsequently impacts the efficiency of using external technological resources.
In summary, this paper argues that after technology mergers and acquisitions, adequate integration measures are taken on acquired technological resources by increasing R&D investment, which leads to a growth trend in firm performance. Based on this analysis, this paper proposes the hypothesis:
H2 : R&D investment mediates the effect of technology mergers and acquisitions on firms’ innovation performance.
3.1 sample selection and data source.
This paper selects the information on M&A events of Chinese A-share listed pharmaceutical companies from 2012 to 2022 as the research sample. Based on data availability and accuracy, this paper excludes ST and *ST, PT, and companies with missing relevant data. For multiple M&A events of the same company in the same year, the first M&A event in a year is selected. Finally, 1,418 unbalanced panel observations for 145 firms that meet the requirements are obtained. To eliminate the impact of outliers on the study, this paper uses Stata16.0 software to perform bilateral shrinkage of the relevant variables. The financial data of listed companies are mainly obtained from the China Stock Market and Accounting Research Database (CSMAR, https://data.csmar.com/ ) and Juchao Information Network 1 to find the annual reports of enterprises. Patent data comes from the China Research Data Service Platform (CNRDS, https://www.cnrds.com/ ).
3.2.1 explained variable.
The paper studies the effects of technology mergers and acquisitions on enterprise performance by thoroughly analyzing innovation and financial performance variables. It refers to the study by Gu and Xie ( 42 ) which selects return on assets (ROA) as the metric to evaluate corporate financial performance. Patents are highly effective in measuring innovation performance, as they provide significant exclusivity and can explain the rise in performance output resulting from technological innovation. A greater quantity of patent applications typically signifies a higher degree of innovation performance exhibited by a company. Experts commonly assert that the quantity of patent applications provides a more accurate indication of the extent of innovation compared to the number of grants. This is due to the fact that patent approvals necessitate evaluation and payment of yearly fees, leading to greater unpredictability and volatility. Thus, this study uses the total number of patent applications increased by one as the logarithm of the innovation performance indicator for measurement.
Technology mergers and acquisitions (Tma) is a dummy variable, assigned a value of 1 if a technology merger or acquisition occurs in an enterprise. Otherwise, it is assigned a value of 0. Technology mergers and acquisitions provide a direct means of accessing the technological resources of the target firm and achieving the substitution and complementation of production technology. This paper defines technology M&A according to Ahuja and Katila ( 43 ). Technology M&A refers to M&A events involving listed businesses that meet one of the following three criteria: (i) the announcement of the M&A by the businesses listed clearly states that the goal of the M&A is to get technology. (ii) The target company possesses patented technology within 5 years prior to the date of the M&A announcement. (iii) The listed companies are classified as high-tech enterprises. This study utilizes the CSMAR database to extract the 2012–2022 M&A information table of listed businesses. We next manually examine the relevant papers to ascertain if the M&A events fall under the category of technological M&A. After applying the aforementioned criteria, we obtained a total of 293 samples of M&A events that satisfy the specified conditions.
Research and development (RD) investment. To measure R&D investment, the empirical practice of Guo ( 44 ) measures the R&D intensity of enterprises by the proportion of R&D expenditure to operating revenue.
The magnitude of a company’s assets directly impacts its capacity to effectively incorporate post-merger technology. Information asymmetry can create market flaws that lead to financing limits for organizations. Companies with large levels of debt typically incur more risks, which in turn restrict their involvement in technological mergers and acquisitions. Companies that experience higher rates of growth in their operating revenue typically possess stronger skills for achieving growth. They are more likely to be preferred by the capital market in mergers and acquisitions. In this paper, we cite the works of Hui et al. ( 45 ), Wang and Huang ( 46 ), and Hong ( 47 ) to regulate the variables of firm size, asset-liability ratio, financing constraints, operating income growth rate, and total asset turnover. This is done to enhance the scientific rigor and dependability of the study by managing other potential confounding factors. See Table 1 for specific measurements.
Table 1 . Variables and their definitions.
3.3.1 fixed effects model.
To test the impact of technology mergers and acquisitions on corporate performance, this paper refers to the research of Hou ( 48 ). It combines theoretical analyses and the design of research indicators to construct the following econometric model:
Where i denotes individual firms, t denotes the year, and Y it s dependent variables are financial performance (Roa) and innovation performance (Invia). The independent variables are technology mergers and acquisitions (Tma) and all other possible control variables (controls), respectively. firmi and year t denote individual and time-fixed effects, respectively, and ε i , t is a random error term. Considering that the individual perspective of pharmaceutical enterprises is not affected by time (enterprise ownership, high-tech enterprise qualification) and the time perspective is not affected by individual changes in the enterprise (industrial structure, GDP growth, years of education of the provincial population, and the macroeconomic environment), and thus the empirical design includes the enterprise individual fixed effects and the year fixed effects, the constructed model (1) is a bidirectional fixed effects model.
To explore the relationship between technology mergers and acquisitions, R&D investment, and enterprise performance, according to the three-step mediating effect model proposed by Wen et al. ( 49 ), this paper adds the mediating variable R&D investment (Rd) based on the above-fixed effect model (1), and constructs the model as follows:
In Equation (2) , RD it is the mediating variable R&D input, and the rest of the symbols have the same meaning as in Equation (1) above Equation (3) , is based on Eq. (1) , with the addition of the variable of R&D investment, which is used to test the effect of technology mergers and acquisitions and R&D investment on corporate performance.
4.1 descriptive statistics analysis.
The descriptive statistics of each variable are shown in Table 2 . It can be seen that the return on assets (Roa) of enterprises ranges from −0.154 to 0.226, with an average value of 0.061, indicating that the financial status of enterprises varies greatly. The number of invention patent applications (Invia) ranges from 0 to 1.857, with an average of 0.893, indicating some variation in firms’ innovation performance and that most pharmaceutical firms have low innovation performance. For the explanatory variables, the mean value of Technology Mergers and Acquisitions (Tma) is 0.207, and the variance is 0.405. The range of Research and Development (RD) is 0.32 ~ 23.06, and the variance is 3.892, which is a significant difference, indicating a great difference in Research and Development (RD) among enterprises. There is a great fluctuation in R&D investment in each company and each year. The maximum value of the capital debt ratio (Lev) is 0.815, and the minimum value is 0.042, indicating that the debt capacity of listed companies is not uniform. The minimum value of enterprise growth (Gro) is -0.46, and the maximum value is 1.162, indicating that listed companies’ development ability and growth opportunities in China’s pharmaceutical manufacturing industry vary more significantly. The value of financing constraint (SA) ranges from -4.747 to-3.233, and the larger the value of SA, the larger the financing constraint. The average value of financing constraint is-3.887, which shows that enterprises generally face the dilemma of financing constraint.
Table 2 . Descriptive statistics.
The results of the Pearson correlation analysis between the variables are shown in Table 3 . It can be seen that the correlation coefficient between technology mergers and acquisitions (Tma) and enterprise financial performance (Roa) is 0.351, and the coefficient with innovation performance (Invia) is 0.336; there is a significant positive correlation. This indicates that with the increase in technology mergers and acquisitions, enterprise performance will also improve, which initially verifies H1. Research and development investment (RD) also has a significant positive correlation with enterprise performance (Roa) and innovation performance (Invia), which initially verifies H2. The study results show that the variance inflation factor VI F value is less than 10, and the absolute value of correlation coefficients between the rest of the variables is less than 0.8, indicating that the variables passed the multiple covariance test.
Table 3 . Correlation analysis.
A fixed effects model is used to test the impact of technology mergers and acquisitions on pharmaceutical firms’ performance, and the regression results of model (1) are shown in Table 4 . Column (1)–Column (4) regressions are all controlled for individual and time effects. Columns (1) and (2) are the effects of technology mergers and acquisitions on the financial performance of enterprises. In column (1), the regression coefficient of technology mergers and acquisitions (Tma) on Roa is 0.0552, which is significant at the 1% statistical level. In column (2), after adding control variables, the coefficient is 0.0149 and still significant at the 1% level, which means that technology mergers and acquisitions can significantly contribute to the enhancement of the financial performance of pharmaceutical enterprises. This implies that technology mergers and acquisitions can significantly promote the financial performance of pharmaceutical companies. Columns (3) and (4) show the effect of technology M&A on innovation performance. Column (3) does not include control variables, and column (4) adds control variables. The regression coefficients of technology M&A are 0.3885 and 0.0881, respectively, which are significant at the 1 percent confidence level, indicating that technology M&A can also enhance the innovation performance of pharmaceutical enterprises; this validates H1. Based on theoretical analysis, enterprises that acquire technology resources for M&As pay more attention to integrating technology and knowledge to quickly absorb the acquired enterprise’s technical knowledge. It can better enhance corporate performance by expanding the scale of their basic knowledge. In addition, by comparing the regression findings in columns (2) and (4), it is evident that technology M&A has a more pronounced impact on pharmaceutical firms’ innovation performance than its effect on financial performance. It could be because technology mergers and acquisitions may prompt firms to adjust their patent policies. Companies can evaluate and modify their patent portfolios based on market demand and technological advancements throughout the merger and acquisition process. As a result, companies may choose to augment their patent applications to align with the changing market conditions and competitive forces.
Table 4 . Benchmark regression results.
Table 5 shows the regression results of research and development investment (RD) as a mediating variable. According to the three-step method of mediating effect, columns (1) and (4) are the results of the benchmark regression of Tma on firm performance, consistent with the above results. Column (2) shows the effect of technology mergers and acquisitions on RD, and the regression coefficient of RD is 1.134 and is significantly positive at the 1% level. It indicates that technology mergers and acquisitions positively impact enterprise R&D investment, and enterprise R&D investment is significantly improved after the enterprise carries out technology mergers and acquisitions. Firms obtain new R&D capabilities and technological knowledge through technology mergers and acquisitions. To fully use these new R&D capabilities, firms increase their R&D investment to develop more innovative products and technologies. Columns (3) and (5) show the effects of technology M&A on firms’ financial performance and innovation performance after adding the mediating variable of RD, respectively. The coefficients of Tma on Roa are 0.00999, which is significantly positive at a 10% statistical level, respectively. The coefficient of Tma on Invia is 0.0673, which is significantly positive at a 5% statistical level. This indicates that R&D investment mediates the effect of technology mergers and acquisitions on enterprise performance, thus verifying the above H2. After a technological merger and acquisition, boosting R&D investment can expedite the company’s integration and assimilation of external technology resources and core knowledge skills. Enterprises can enhance their level of technological innovation by bolstering internal research and development efforts and aggressively leveraging them. Simultaneously, the surge in research and development spending resulting from technological M&A aids firms in effectively adapting to shifts in market demand, thereby enhancing their performance.
Table 5 . Regression results mediated by R&D investment.
In addition, the model passed the Sobel test and Bootstrap test (drawing self-help samples 1,000 times), and the test results are shown in Table 6 . When the explanatory variable is Roa, the direct effect coefficient is 0.009992, and the indirect effect coefficient is 0.00492, which is significantly positive at 1%. When the explanatory variable is Invia, the direct effect coefficient is 0.06782, and the indirect effect coefficient is 0.20802, both of which are significant at 1%. It indicates that research and development investment (RD) as a mediating variable promotes the positive effect of technology mergers and acquisitions on firms’ financial performance, further validating H2.
Table 6 . Mediating effect model Sobel and Bootstrap test.
4.5.1 replacement of variable indicators.
Referring to the study of Wang and Huang ( 46 ), this paper replaces the dummy variable of whether the explanatory variable is mergers and acquisitions (Tma) with the ratio of the sum of the amount of all technology merger and acquisition deals initiated by listed companies in the year to the total assets (Ta) to conduct the regression again, and the results are shown in columns (1) and (2) of Table 7 . The coefficient of Roa is 0.1517, the coefficient of Invia is 0.2078, and the regression coefficients of firm performance are all significantly positive at the 1% level. This indicates that technology mergers and acquisitions positively impact firm performance, and the regression results are consistent with the benchmark regression results. The explanatory variable return on assets (Roa) is replaced by return on equity (Roe), and the regression results are shown in column (3) of Table 7 . The coefficient of Roe is 0.0159, which is significantly positive at the 10 percent level. The regression results are consistent with those of the benchmark regression results, which indicates that this paper’s conclusion on the promotional effect of technology mergers and acquisitions on corporate performance is robust.
Table 7 . Robustness test.
Referring to the empirical research method of Li and Yang ( 50 ), this paper transforms the unbalanced panel data into balanced panel data for regression to ensure sample integrity. The regression results are shown in columns (4) and (5) of Table 7 . The coefficient of Roa is 0.0244, the coefficient of Invia is 0.3121, and the coefficient of technology M&A on firm performance is still significantly positive and significant at the 1% level, indicating that the benchmark regression results are robust and reliable.
To avoid the influence of problems such as autocorrelation and heteroskedasticity, this paper refers to the study of Zhang et al. ( 51 ), adjusts the heteroskedasticity and clustering of the standard errors, and shows the results in Table 7 . From the regression results in Columns (6) and (7), it can be seen that, after the adjustments to the standard errors, the promotional effect of technology M&As on firm performance is still significant, which once again verifies the robustness of the conclusions of the paper’s study.
The endogeneity problem is usually a 3-pronged problem of omitted variables, bi-directional causality, and measurement error in the variables. To eliminate the possibility of endogeneity problems, this paper adopts instrumental variables and the dynamic panel system generalized moment estimation (SYS-GMM) method for testing. This paper refers to the study of Li et al. ( 52 ) and adopts the explanatory variables (Tma) lagged term (L.Tma) as an instrumental variable, which can keep the obvious correlation between it and the explanatory variables, and also avoid the problem of weak instrumental variables. In addition, the current period’s disturbance terms cannot affect these lagged indicators. Therefore, the instrumental variables are selected to lag the lagged terms of the explanatory variables, which can satisfy the constraints of correlation and homogeneity.
Table 8 shows the results of the instrumental variable test. After controlling for possible endogeneity issues by choosing this instrumental variable, the Roa and Invia coefficients are still positive, the level of significance remains unchanged, and technological M&A still present significant positive incentives for firm performance. The results of this test once again maintain the findings of the previous study, indicating that the results are robust and credible.
Table 8 . Endogeneity test (instrumental variable approach).
In addition, this paper incorporates the lagged one-period of the explanatory variables into the regression model to further address possible endogeneity issues through the SYS-GMM approach. In the SYS-GMM estimation, we consider the lagged one period of the explanatory variables and technology mergers and acquisitions as endogenous variables and use the lagged terms of the explanatory variables as instrumental variables. Roodman ( 53 ) emphasizes that the HansenTest is more robust than the SarganTest regarding heteroskedasticity problems in the model. Hence, this paper reports the results of the HansenTest. The results of the endogeneity test are shown in Table 9 , where we find that the regression coefficients of the explanatory variables return on total assets (Roa) lagged one period is 0.1442, which is significantly positive at the 10% level. The coefficient of the number of invention patent applications (Invia) is 0.2269, which is significantly positive at the 5% level. This indicates that there is an inertia in the firms’ technology M&A decisions and that the outcome of the decision in the previous period significantly affects the technology M&A decisions in the next period. The regression coefficients of the explanatory variable technology mergers and acquisitions (Tma) are 0.0315 and 0.3621, respectively, which are still significantly positive at the 1 percent confidence level. To enhance the reliability of the SYS-GMM estimation results, the rationality of the model setup and the validity of the instrumental variable selection are examined in this paper, respectively. Among them, the test results of AR(2) for second-order serial correlation show that the original hypothesis cannot be rejected, indicating that there is no second-order serial correlation in the residual terms of the dynamic panel. Also, the results of the HansenTest for the test of whether there is over-identification of instrumental variables indicate that the instrumental variables used in the model are appropriate.
Table 9 . Endogeneity test (SYS-GMM).
5.1 heterogeneity in the nature of corporate equity.
Organizational structures and management styles may vary between firms due to equity variances. This paper examines the nature of equity based on information from the actual controller of listed companies in the Cathay Pacific database. The sample is divided into state-owned enterprises and non-state-owned enterprises, including private, foreign, and other types of enterprises. The data is then analyzed using a fixed effects model for regression to determine the difference in the impact of technology mergers and acquisitions on enterprise performance.
Table 10 shows the results of the equity heterogeneity test. Overall, for both state-owned and non-state-owned enterprises, technology mergers and acquisitions significantly impact enterprise performance, which verifies the robustness of the benchmark regression results. Specifically, compared to state-owned firms, technological M&As have a higher positive impact on the performance of non-state-owned enterprises. According to the theoretical study, this could be because state-owned firms have a more comprehensive range of reasons for engaging in technological mergers and acquisitions, and they prioritize objectives other than innovation performance. Non-state-owned enterprises typically encounter heightened market competition and prioritize assimilating technology and knowledge following technology mergers and acquisitions. It enables the enterprises to swiftly incorporate the acquired company’s technological expertise, enhancing benefits. In addition, non-state-owned firms exhibit greater adaptability and prowess in innovation than state-owned counterparts. State-owned firms could face additional legislative limitations and regulations, which could impede their ability to innovate. Technology M&As can offer non-state-owned companies access to fresh technology and expertise, enabling them to enhance efficiency, save expenses, and innovate new products.
Table 10 . Equity heterogeneity regression results.
The size of a firm is a crucial aspect that affects how technology mergers and acquisitions impact the performance of the firm. This paper categorizes the enterprise’s total assets at the end of the period into two groups: the first group includes large-scale enterprises with total assets in the first three quartiles of the size distribution, while the second group includes small and medium-sized enterprises with the remaining total assets.
Table 11 shows the regression outcomes for various company sizes, indicating that technological M&As have a substantial impact on the performance of both large-scale and small-and medium-sized pharmaceutical companies. Specifically, technology M&As have a more significant impact on enhancing innovation performance in small and medium-sized companies than in large-scale organizations. This is because small-scale enterprises typically possess a more uniform business plan, product assortment, and a very uncomplicated management structure. They can respond and take action with incredible speed and adaptability when confronted with technology mergers and acquisitions. Furthermore, they are eager to capitalize on the opportunity presented by technology M&As to enhance technological innovation. However, the inflexibility inherent in the hierarchical structure of large-scale corporations, as opposed to small-scale enterprises, diminishes the motivation for technical innovation. In addition, large corporations own more advanced infrastructure, typically have more sophisticated innovation frameworks, and may require less emphasis on innovation. Technology mergers and acquisitions are more effective in fostering the growth of large-scale enterprises in terms of financial performance. Based on the theoretical analysis, this may be attributed to variations in the knowledge resources, the ability to integrate and absorb new information, and the capability to finance research and development among firms of varying sizes. Technology M&As frequently necessitate a significant financial commitment to support ongoing research and development spending and the integration of resources. Major corporations consistently secure additional research and development funding following a technology merger and acquisition, whereas smaller companies may encounter financial limitations shortly after completing a technology merger and acquisition. Small-sized enterprises may lack the financial resources to cover the increased expenses of mergers and acquisitions and the consequent investment in research and development. Furthermore, small-scale firms often have limited research and development capabilities and struggle to effectively integrate resources, which contrasts with the more robust capabilities of large-scale enterprises. This disparity can result in inadequate technical integration and innovation following a merger or acquisition, ultimately impacting the financial performance of the enterprises.
Table 11 . Regression results of size heterogeneity.
The geographical distribution of pharmaceutical firms in China is divided into several regions. Therefore, we categorize the listed pharmaceutical companies in our sample into three groups: East, Central, and West. The sample consists of 648 pharmaceutical enterprises in the East area, 279 in the Central region, and 208 in the West region. Subsequently, we assess the influence of technological mergers and acquisitions on the corporate performance of pharmaceutical firms in these three geographical areas.
Table 12 shows the outcomes of the examination of regional heterogeneity. The regression analysis indicates that technological M&As have a substantial impact on the performance of pharmaceutical companies in the central, eastern, and western areas. More precisely, the impact of technological M&As on the performance of companies is more noticeable in the eastern region than in the central and western areas. The reason for this could be attributed to the fact that the east part of China is primarily located along the coastline, characterized by a flat topography, efficient transportation infrastructure, and the presence of numerous prominent domestic pharmaceutical companies. Consequently, this region has become a magnet for drawing many highly skilled professionals ( 54 ). Pharmaceutical businesses in the eastern area are increasing their collaboration and engagement with major multinational pharmaceutical corporations, displaying greater agility in responding to industry dynamics and adopting a more proactive strategy toward mergers and acquisitions. Relatively speaking, China’s central and western areas began to engage with the international community later, and their infrastructure development is comparatively less advanced. The natural environmental conditions in this area are subpar, and its economic progress is sluggish, characterized by a scarcity of technology, skilled individuals, and financial resources. In addition, according to the theoretical aspect of the preceding analysis, the central and western regions exhibit a generally low level of technology, resulting in limited capacity for digestion and absorption and a comparatively poor technological knowledge base. Consequently, it is difficult to bring about major technological breakthroughs for remote regions because they may struggle to completely comprehend and integrate the company’s technological advancements following the merger and acquisition.
Table 12 . Geographical heterogeneity regression results.
6.1 conclusion.
The macro background of China’s economic structural transformation requires enterprises to have more robust technological innovation capabilities. Technology mergers and acquisitions (M&A) are ineffective strategies to occupy a favorable market position. This paper empirically analyses the impact of technology mergers and acquisitions (M&A) on enterprise performance with the sample data of Chinese pharmaceutical-listed enterprises from 2012 to 2022. The conclusions are as follows: (i) Benchmark regression results show that technology mergers and acquisitions (M&A) have a significant positive impact on the performance of pharmaceutical enterprises. (ii) Based on the mediation effect model, it is found that there is a mediation effect of R&D investment in the impact of technology M&As on enterprise performance. (iii) Through subgroup regression, the effect of technology M&A on pharmaceutical firm performance is heterogeneous regarding the nature of equity, firm size, and geographic region. Regarding different equity natures, technology M&A has a more vital role in promoting the performance of non-state-owned enterprises than state-owned enterprises. Regarding firm size, technology M&As have a more substantial effect on the innovation performance of small and medium-sized enterprises than large firms and a stronger effect on the financial performance of large firms than small and medium-sized enterprises. Regarding different regions, technology M&As are more effective in promoting the performance of pharmaceutical enterprises in the eastern region.
Based on the above theoretical and empirical results, combined with the status quo of technology M&A activities and R&D investment of China’s listed pharmaceutical companies, this paper proposes suggestions from both the government and enterprise levels.
At the governmental level, it is imperative for the government to enhance the focus on technology M&As to foster enterprise innovation. Additionally, the government should provide guidance and incentives to pharmaceutical companies to improve their performance using technology M&A. Acknowledging the diversity of technology M&As in enhancing innovation performance across organizations with varying ownership structures, sizes, and geographical locations is essential. When implementing the policy, it is crucial to prioritize efficiency and balance to expedite achieving the goal of supporting technological innovation and enterprise development. It will ensure that technology mergers and acquisitions activities provide the most favorable outcomes. Specifically, the government should consider the different behavior of enterprises with different property rights in the face of technology mergers and acquisitions. The government should provide state-owned firms a permissive environment for technology mergers and acquisitions. It is recommended that the current obstacles preventing state-owned firms from participating in technology M&A be removed by streamlining the administrative license and approval processes. Enterprise innovation necessitates substantial cash; the government can implement policies such as providing financial subsidies and tax incentives. It can alleviate the difficulties encountered by enterprises in the process of technology mergers and acquisitions by strengthening the government’s financial support. Secondly, in the face of the differences between pharmaceutical production enterprises of different sizes. The Government should provide guidance and support to foster the growth of innovative and competitive small and medium-sized firms in the industry. It will modify its policy’s flexibility to enhance the diverse market demand by setting aside a specific market share for qualified businesses. The Government also stimulates large-scale enterprises to merge and acquire overseas high-tech enterprises and integrate domestic resources by establishing special funds and technical support measures to promote competitiveness and innovation in the pharmaceutical market. Finally, the government should fully consider the actual differences in the situation of pharmaceutical production enterprises in the eastern, central, and western regions. It should strengthen support for the M&A and innovation system in those areas and facilitate technical mergers and acquisitions by bringing in talent and bolstering infrastructure to boost innovation in the central and western regions. Simultaneously, the government should establish a cross-regional collaboration platform for enterprises in the eastern, central, and western areas to facilitate the sharing of resources and foster collaborative innovation. This platform encourages the involvement of enterprises from the central and western regions in merger and acquisition activities to enhance their capability and success rate in M&A endeavors.
At the company level, it is crucial for companies to recognize the significance of innovation to differentiate themselves in the competitive market. Technology M&A and investment in R&D are successful strategies for organizations to obtain innovative resources and improve their ability to innovate technologically in response to changes in their business models. State-owned enterprises should leverage their resources and fully capitalize on their strengths. They should demonstrate the bravery to expand internationally and actively engage in mergers and acquisitions to foster innovation. In addition, enterprises must comprehend cutting-edge market trends, consistently acquire and assimilate sophisticated technological expertise, and augment their consciousness of innovation and research and development capabilities. Non-state-owned enterprises must align with national strategies, persist in exploring and innovating, and enhance their investment in research and development. They should focus on innovation and quality and offer a wide range of items to cater to consumers’ diverse needs to gain a competitive edge in the market. It will help them avoid competing with similar products. Large enterprises should leverage their extensive technological expertise and consistently innovate by building upon their existing technology to improve the level of R&D and enhance the core competitiveness of firms in the industry. Small and medium-sized enterprises should evaluate their capacity for growth and determine if they can obtain external technological resources by engaging in technology mergers and acquisitions. It is essential to thoroughly assess the potential risks and advantages of technology M&As and develop a comprehensive M&A strategy that effectively incorporates technology and accelerates technology upgrading smoothly. Also, enhancing the accumulation of financial resources and human capital is imperative. They should exercise stringent control over the allocation of research and development funding, attract exceptionally skilled individuals to increase the scope of corporate knowledge, and strengthen the fundamental competitiveness of firms. In addition, it is crucial to be mindful of market trends and fully comprehend the cutting-edge advancements within the sector. Pharmaceutical enterprises in the central and western regions should enhance their collaboration with external firms that possess robust research and development capabilities, as well as universities and research institutes. By combining industry, academia, and research, they can enhance the existing technology level, breaking through the bottleneck and improving the success rate of technology M&A. At the same time, enterprises should consider improving the welfare treatment of talents and strengthening the training of talents to ensure the long-term development of enterprises.
This study still has many shortcomings, which may also be worth further exploration. First, this paper only studied the pharmaceutical manufacturing industry in China. Although pharmaceutical manufacturing enterprises are typical representatives of the real economy and high-tech economy, the research object is still limited and can expand the scope of the study to other industries in other countries in the future. Second, due to the availability of data, this paper only considered listed pharmaceutical enterprises, and the phenomenon of technological mergers and acquisitions also exists in non-listed pharmaceutical enterprises. Future research can turn to unlisted companies to fully understand the relationship between technology M&A and firm performance. Third, this paper only analyzes the correlation from the relationship between technology M&A, R&D investment, and firm performance, but in practice, there may be other unobserved factors (such as market competition, managerial decision-making, etc.) affecting firm performance, so it is necessary to explore the correlation analysis more deeply in the future.
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
JY: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft. JL: Methodology, Validation, Writing – review & editing. SW: Supervision, Writing – review & editing. YC: Supervision, Writing – review & editing.
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Liaoning Provincial Department of Education’s 2024 Innovative Development Project for Scientific Research, the Liaoning Provincial Social Science Planning Fund Project (Approval No. L23BGL006), and the Philosophy and Social Science Research Base Project of Shenyang City Social Science Federation (Approval No. SYSK2024-JD-24).
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.
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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Keywords: technology M&A, corporate performance, R&D, pharmaceutical industry, mediating effects
Citation: Yang J, Li J, Wang S and Chen Y (2024) Research on the impact of technology mergers and acquisitions on corporate performance: an empirical analysis based on China’s pharmaceutical industry. Front. Public Health . 12:1419305. doi: 10.3389/fpubh.2024.1419305
Received: 18 April 2024; Accepted: 19 July 2024; Published: 09 August 2024.
Reviewed by:
Copyright © 2024 Yang, Li, Wang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Su Wang, [email protected] ; Yuwen Chen, [email protected]
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Research on aerodynamic characteristics of three offshore wind turbines based on large eddy simulation and actuator line model.
2. numerical method, 3. verification and validation, 3.1. single turbine parameter settings, 3.2. results of the mesh independence test, 3.3. comparison and validation with existing literature results, 4. three-turbine array configuration, 4.1. three-turbine configuration and computational domain grid division under full wake cases, 4.2. cases of three turbines under staggered arrangement, 5. comparative analysis of three-turbine simulation results, 5.1. three turbines under full wake conditions, 5.1.1. comparison of aerodynamic performance results, 5.1.2. comparison of wake characteristics results, 5.1.3. unsteady aerodynamic characteristics of three turbines under full wake conditions, 5.2. three turbines under staggered cases, 5.2.1. comparison of aerodynamic performance results, 5.2.2. comparison of wake characteristics results, 5.2.3. unsteady aerodynamic characteristics of three turbines under staggered conditions, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.
Nomenclature | |
C | Thrust coefficient [-] |
C | Power coefficient [-] |
Mean thrust coefficient [-] | |
Mean power coefficient [-] | |
L | Distance between the upstream and downstream turbines [m] |
D | Rotor diameter [m] |
k* | Turbulent kinetic energy [J/kg] |
P | Power output [MW] |
Q | Torque of the rotor [N·m] |
T | Thrust of the rotor [kN] |
S | Distance in the Y direction between the turbines [m] |
U | Wind speed [m/s] |
Mean wake velocity [m/s] | |
Greek letters | |
ρ | Fluid density [kg/m ] |
λ | Tip speed ratio |
Ω | The rotation speed of the rotor [r/s] |
Abbreviations | |
3D | Three dimensional |
SGS | Subgrid-scale |
FRM | Fully resolved mesh |
BEM | Blade element momentum theory |
CFD | Computational fluid dynamics |
LES | Large Eddy Simulation |
ALM | Offshore floating wind turbine |
TSR | Tip speed ratio |
TI | Turbulence intensity |
RANS | Reynolds Averaged Navier–Stokes |
WT1 | Wind turbine 1 |
WT2 | Wind turbine 2 |
WT3 | Wind turbine 3 |
Click here to enlarge figure
NREL 5 MW | Units | |
---|---|---|
Rated power | 5.0 | MW |
Rotor diameter | 126.0 | m |
Number of blades | 3 | |
Hub diameter | 3.0 | m |
Hub height | 90 | m |
Rated wind speed U | 11.4 | m/s |
Rated rotor speed | 12.1 | rpm |
Total Mesh | R/Δ | C | Relative Difference (%) | |
---|---|---|---|---|
Coarse mesh | 3.62 × 10 | 28 | 0.5502 | 4.30 |
Medium mesh | 6.52 × 10 | 36 | 0.5275 | 0.64 |
Fine mesh | 8.86 × 10 | 40 | 0.5241 | / |
Case | L /D | S/D | ||
---|---|---|---|---|
1 | Full wake cases | 4~7, interval = 0.5 | 0 | |
2 | Staggered cases | 4~6, interval = 0.5 | 2 | |
3 | Staggered cases | 4~6, interval = 0.5 | 2 |
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Fu, C.; Zhang, Z.; Yu, M.; Zhou, D.; Zhu, H.; Duan, L.; Tu, J.; Han, Z. Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model. J. Mar. Sci. Eng. 2024 , 12 , 1341. https://doi.org/10.3390/jmse12081341
Fu C, Zhang Z, Yu M, Zhou D, Zhu H, Duan L, Tu J, Han Z. Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model. Journal of Marine Science and Engineering . 2024; 12(8):1341. https://doi.org/10.3390/jmse12081341
Fu, Chen, Zhihao Zhang, Meixin Yu, Dai Zhou, Hongbo Zhu, Lei Duan, Jiahuang Tu, and Zhaolong Han. 2024. "Research on Aerodynamic Characteristics of Three Offshore Wind Turbines Based on Large Eddy Simulation and Actuator Line Model" Journal of Marine Science and Engineering 12, no. 8: 1341. https://doi.org/10.3390/jmse12081341
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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 ...
Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation. Characteristics of a good hypothesis
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 ...
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
hypothesis. science. 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 ...
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
3.4 Hypotheses. When researchers do not have predictions about what they will find, they conduct research to answer a question or questions with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses.
A research hypothesis helps test theories. A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior. It serves as a great platform for investigation activities.
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation ('x affects y because …'). A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses.
A hypothesisis a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
18. Hypotheses. When researchers do not have predictions about what they will find, they conduct research to answer a question or questions, with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses.
First, a good hypothesis must be testable and falsifiable. We must be able to test the hypothesis using the methods of science and if you'll recall Popper's falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical.
The researcher states a hypothesis to be tested, formulates an analysis plan, analyzes sample data. according to the plan, and accepts or rejects the null hypothesis, based on r esults of the ...
A hypothesis (from the Greek, foundation) is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator's thinking about the problem and, therefore, facilitates a solution. Unlike facts and assumptions (presumed true and, therefore, not ...
First, a good hypothesis must be testable and falsifiable. We must be able to test the hypothesis using the methods of science and if you'll recall Popper's falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical.
A hypothesis is a statement of the researcher's expectation or prediction about relationship among study variables. The research process begins and ends with the hypothesis. It is core to the ...
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
The frequentist hypothesis or the traditional approach to hypothesis testing is a hypothesis testing method that aims on making assumptions by considering current data. The supposed truths and assumptions are based on the current data and a set of 2 hypotheses are formulated.
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Through this definition, it can be known that the OCB conducted by members of the organization aims to support the functionality of their organization, both in normal and abnormal situations. The normal situation in question is the absence of things that have the potential to hinder the organization from conducting its functions.
The "Research Design" section provides data sources, variable selection, and model setup. The "Empirical Analysis" and "Heterogeneity Analysis" sections discuss the results. Finally, "Conclusions and Recommendations, Shortcomings and Prospects" is given. 2 Literature review and research hypothesis
Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)-actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs ...