Theoretical Framework and Hypothesis Development

THE GENERATION OF THEORY

It is now feasible to see how we can create the theoretical framework for our research after looking at the various variable types that can function in a scenario and how the relationships among these can be established.

The theoretical framework forms the cornerstone of the deductive research project. It is a rationally constructed, explained, and elaborated network of relationships between the variables that have been found to be pertinent to the issue at hand and have been identified through procedures like literature reviews, observations, and interviews. The theoretical framework's development is also guided by intuition and experience.

At this point, it becomes clear that in order to come up with effective solutions, the issue must be accurately identified before moving on to identify the contributing variables. It is now evident how important it is to perform an exhaustive literature review as well as exploratory and inductive research. The next stage after selecting the right variables is to describe the network of relationships between them so that pertinent theories can be created and then put to the test. The degree to which the problem can be solved is made clear by the outcomes of hypothesis testing, which show whether or not the hypotheses have been supported. Thus, developing a theoretical framework is a crucial stage in the research process. The theoretical framework and the literature review are related in that the former serves as a strong foundation for the development of the latter. In other words, based on the results of earlier studies, the literature review determines which variables may be significant.

This serves as the foundation for the theoretical model, along with other logical connections that can be imagined. The relationships between the variables are represented and elaborated upon by the theoretical framework, which also elucidates the theory behind these relationships and characterizes their nature and direction. A strong theoretical framework is established by the literature review, which also offers the rational foundation for the development of testable hypotheses.

THE ELEMENTS THAT MAKE UP THE THEORETICAL FRAMEWORK

A strong theoretical framework describes and explains the relationships between the significant variables in the scenario that are pertinent to the issue after defining and identifying them. The relationships between the dependent variable(s), the independent variables, and the moderating and mediating variables (if any) are explained in detail. If a moderating variable or variables exist, it is crucial to describe the specific relationships they moderate as well as how they work. They ought to provide a justification for their moderating actions as well. If any mediating variables exist, it is required to discuss how or why they are regarded as such.

Should there be any mediating variables, it is imperative to have a discourse regarding how or why they are regarded as such. In the event that there are two or more dependent variables, any relationships between the independent variables or the dependent variables themselves should also be explicitly stated and sufficiently explained. Keep in mind that a complex theoretical framework is not always a sign of quality.

Any theoretical framework ought to have these three fundamental components: 1. It is important to define the variables that are thought to be pertinent to the study.

2. It is necessary to provide a conceptual model that explains how the model's variables relate to one another.

3. A detailed justification for our expectations should be provided.

Coming up with widely accepted definitions for the pertinent variables is not always simple. For example, there are literally dozens of definitions of "brand image," "customer satisfaction," and "service quality" available in the marketing literature. Generally speaking, there are many definitions available in the literature. Nonetheless, carefully considered guiding definitions of concepts are required, as they will assist you in explaining the connections among the variables in your model. Furthermore, during the data collection phase of the research process, they will also act as a foundation for the operationalization or measurement of your concepts. Because dictionary definitions are typically too general, you will need to select a useful definition from the literature. It's important that you explain why you have chosen a particular definition as your guiding definition.

You can better organize your literature discussion by using a conceptual model.

A conceptual model explains your theories regarding the relationships between the concepts (variables) in your model. A conceptual model's schematic diagram gives the reader a quick understanding of the theorized relationships between the variables in your model and how you believe the management problem can be resolved. Thus, this is how conceptual models are typically expressed. But verbal expressions are also a suitable means of expressing relationships between variables. To make the theorized relationships easy for the reader to see and understand, both a schematic diagram of the conceptual model and a written description of the relationships between the variables should be provided.

Discussions regarding the connections between the variables in your model are facilitated and stimulated by this. Therefore, it's critical that the foundation of your model is a solid theory.

Finally, the theoretical framework consists of a theory, or a coherent explanation for the relationships in your model. A theory aims to clarify the connections between the variables in your model; it should explain each significant relationship that is hypothesized to exist between the variables. If it is possible to theorize the nature and direction of the relationships based on prior research findings and/or your own ideas, then this should indicate whether the relationships should be positive, negative, linear, or nonlinear.

The development of testable hypotheses to determine the validity of the proposed theory can then be done from the theoretical framework.

It should be noted that you are not required to "invent" a new theory each time you conduct research. Applying current theories to a particular context is what's meant by applied research. In other words, conclusions from earlier research can be made. On the other hand, you will contribute to some extent to current theories and models in a fundamental research setting. It is not (always) possible to use preexisting theories or explanations for relationships between variables in such a situation. Consequently, you will need to rely on your own perceptions and concepts.

HYPOTHESIS DEVELOPMENT

Once the key variables in a scenario have been determined, and their relationships have been established through logical reasoning within the theoretical framework, we can test whether the relationships that have been theorized actually hold true. We are able to gather trustworthy information on the types of relationships that exist among the variables operating in the problem situation by testing these relationships scientifically through appropriate statistical analyses or through negative case analysis in qualitative research (described later in the chapter). The outcomes of these tests provide some indications as to what modifications might be made to the circumstances in order to address the issue. It is known as hypothesis development to formulate such testable statements.

HYPOTHESIS DEFINITION

A hypothesis is a conjectural but testable statement that forecasts what you hope to discover in your empirical data. The theory that forms the foundation of your conceptual model informs your hypotheses, which are frequently relational in nature. Accordingly, hypotheses are defined as logically conjectured relationships between two or more variables that are stated as testable assertions. It is anticipated that solutions to fix the issue will be found by putting the theories to the test and validating the predicted relationships.

STATEMENT OF HYPOTHESES: FORMATS

If–then statements

As was previously mentioned, a testable assertion about the relationship between variables is what is known as a hypothesis. Additionally, a hypothesis can investigate whether there are variations in any of the following between two or more groups: any number of variables. These hypotheses can be expressed as propositions or as if-then statements to test the existence of the conjectured relationships or differences.

DIRECTIONAL AND NONDIRECTIONAL HYPOTHESES

Terms like "positive," "negative," "more than," "less than," and similar expressions are used when comparing two groups or stating the relationship between two variables. These are directional hypotheses because they suggest either the nature of the difference between two groups on a particular variable (more than/less than) or the direction of the relationship between the variables (positive/negative), as in the first example below.

Conversely, nondirectional hypotheses assume a relationship or difference but do not specify which way these relationships or differences will go. Put another way, as in the first example below, even though it may be assumed that two variables have a significant relationship, we might not be able to determine whether the relationship is positive or negative. Similarly, we might not be able to predict which group will differ more and which less on a given variable, even if we can hypothesize that there will be differences between two groups on that variable.

When relationships or differences have never been investigated, there is no foundation upon which to infer a direction; alternatively, nondirectional hypotheses are developed when prior research on the variables has produced contradictory results. It's possible that some investigations discovered a positive relationship, while others may have discovered a negative one. Consequently, the present investigator may be limited to speculating about the existence of a noteworthy correlation, without being able to definitively determine its direction. These situations allow for the non-directional statement of the hypotheses.

NULL AND ALTERNATE HYPOTHESES

Hypotheses must be falsifiable in order to be used in the hypothetico-deductive method; that is, they must be written in a way that allows other researchers to disprove them. This is the reason why null hypotheses occasionally accompany hypotheses. In order to support an alternate hypothesis (labeled HA), a null hypothesis (H0) is one that is intended to be rejected. When applied, the null hypothesis is taken for granted until statistical proof—such as a hypothesis test—indicates otherwise. The null hypothesis might claim, for example, that men and women purchase the same quantity of shoes or that advertising has no effect on sales. To put it more broadly, the null hypothesis could assert that there is no correlation between two variables or that there is a difference in means of two groups in the population is equal to zero (or some other definite number).

A common way to state the null hypothesis is that there is either no (significant) difference between two groups or no (significant) relationship between two variables. As the null hypothesis' opposite, the alternate hypothesis expresses a relationship between two variables or highlights differences between groups.

To clarify further, we are asserting that there is no distinction between the characteristics of the population—that is, the entire group about which we are curious—and the sample—that is, a small subset representative of the entire population or group that we have selected for study—when we establish the null hypothesis. Since we are unaware of the population's actual situation, all we can do is to draw inferences based on what we find in our sample.

By positing the null hypothesis, we are implying that any differences or relationships between variables that we find based on our sample are only the result of random sampling fluctuations and not of any "true" differences between the two population groups (for example, men and women) or between variables (for example, sales and profits). Thus, the null hypothesis is put forth in order to test whether or not it can be rejected. In the event that the null hypothesis is rejected, all other plausible theories pertaining to the specific relationship under investigation may find favor. We can believe in the alternative hypothesis that is developed in a given research investigation because of the theory.

This is just another argument for why the theoretical framework should be founded on strong, defensible reasoning from the outset. If not, other scientists are likely to challenge and offer alternative, plausible theories in place of the rejected explanation.

H0: m m M W = or H0: m m M W - = 0 would be the null hypothesis regarding group differences in the example "Women are more motivated than men," where μM and μW stand for the mean motivational levels of the men and women, respectively.

In the example above, the statistical alternate would be as follows:

HA M : m m < W

which is the same as

HA W M : m m > W

where HA represents the alternate hypothesis and μM and μW are the mean

motivation levels of men and women, respectively.

For the nondirectional hypothesis of mean group differences in work ethic

values in the example “There is a difference between the work ethic values of

American and Asian employees,” the null hypothesis would be:

H0 AM AS : m m =

or

H0 : m m AM AS - = 0

where H0

represents the null hypothesis, μAM is the mean work ethic value of

Americans and μAS is the mean work ethic value of Asians.

The alternate hypothesis for the above example would statistically be set as:

HA AM AS : m m 1

where HA represents the alternate hypothesis and μAM and μAS are the mean

work ethic values of Americans and Asians, respectively.

The null hypothesis for the relationship between the two variables in the

example “The greater the stress experienced in the job, the lower the job satis-

faction of employees,” would be H0

: There is no relationship between stress

experienced on the job and the job satisfaction of employees. This would be

statistically expressed by:

H0 r = 0:

where r represents the correlation between stress and job satisfaction, which

in this case is equal to 0 (i.e., no correlation).

The alternate hypothesis for the above null, which has been expressed direc-

tionally, can be statistically expressed as:

HA : ( r < 0 The correlation is negative.)

For the example “There is a relationship between age and job satisfaction,”

which has been stated nondirectionally, the null hypothesis would be statistically expressed as:

H0 r = 0:

whereas the alternate hypothesis would be expressed as:

HA: r 1 0

The proper statistical tests (t-tests, F-tests) can then be used to determine whether or not support has been found for the alternate hypothesis, which is that there is a significant difference between groups or a significant relationship between variables, as hypothesized, after the null and alternate hypotheses have been developed.

To test a hypothesis, the following procedures must be followed:

1.List both the alternative and the null hypotheses.

2. Depending on whether the data were gathered using parametric or nonparametric methods, select the relevant statistical test.

3. Choose the appropriate level of significance (p = 0.05, or less).

4. Check to see if the computer analysis's output results show that the significance level is satisfied.

Look up the critical values that define the regions of acceptance on the relevant table (i.e., (t, F, c2) – see the statistical tables at the end of this book) if the significance level is not indicated in the printout, as in the case of Pearson correlation analysis in Excel software. The null hypothesis's acceptance and rejection regions are separated by this critical value. The null hypothesis is rejected and the alternative is accepted when the resulting value exceeds the critical value. In case the computed value is lower than the critical value, the alternate is rejected and the null is accepted.

HYPOTHESIS TESTING WITH QUALITATIVE RESEARCH: NEGATIVE CASE ANALYSIS

Qualitative data can be utilized to test hypotheses as well. Assume, for instance, that a researcher has created a theoretical framework following in-depth interviews, which explains why employees engage in unethical behavior: either because they lack the capacity to distinguish between right and wrong, or because they are desperate for additional funding, or because the company doesn't care about these kinds of behaviors. In order to verify the hypothesis that these three factors are the main ones influencing unethical behavior, the researcher needs to find evidence to contradict the hypothesis.

The theory needs to be revised when even one instance contradicts the hypothesis. Let's imagine the investigator discovers a single instance in which a person knowingly engages in the immoral habit of taking bribes (despite the fact that he possesses the knowledge necessary to distinguish between right and wrong, has no need of funds and is aware that the organization won't be in need of any indifferent to his actions), only as a means of trying to "get back" at the system, which "won't listen to his advice." This new finding was made possible by disconfirmation of the initial hypothesis, which is referred to as the negative case method, allows the researcher to make revisions to both the theory and the hypothesis until the theory is strong.

MANAGERIAL IMPLICATIONS

The manager can evaluate the consultant's research report with intelligence if they are aware of the process and goals involved in developing the theoretical framework and generating the hypotheses. Now that the problem has been identified, it is evident that managers can better understand how various factors—the independent variables in the model—may offer potential solutions to the problem—the dependent variable in the model—if they have a solid understanding of the terms "independent variable" and "dependent variable." A comprehension of the notion of "moderating variable" could enable the manager to recognize that certain suggested remedies might not address the issue for every individual or circumstance. Similarly, understanding significance and the reasons behind a given hypothesis is either accepted or rejected, helps the manager to persist in or desist from following hunches, which, while making good sense, do not work. If such knowledge is absent, many of the findings through research will not make much sense to the manager and decision making will bristle with confusion.

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