mbr chap2.docx

8/21/2019 mbr chap2.docx

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The hallmarks of scientific research

The hallmarks or main distinguishing characteristics of scientific research may be listed as follows:

1. Purposiveness

2. Rigor

3. Testability

4. Replicability

5. Precision and confidence

6. Objectivity

7. Generalizability

8. Parsimony

Each of these characteristics can be explained in the context of a concrete example. Let us consider the case of a manager who is interested in

investigating how employees commitmentto the organization can be increased. We shall examine how the eight hallmarks of science apply to this

investigation so that it may be considered "scientific."

Purposiveness

The manager has started the research with a definite aim or purpose. The focus is on increasing the commitment of employees to the organizationas this will be beneficial in many ways. An increase in employee commitment will translate into less turnover, less absenteeism, and probably

increased performance levels, all of which will definitely benefitthe organization. The research thus has a purposive focus.

Rigor

A good theoretical base and a sound methodological design add rigor to a purposive study. Rigor connotes carefulness, scrupulousness, and the

degree of exactitude in research investigations. in the case of our example, let us say the manager of an organization asks 10 to 12 of its employees

to indicate what would increase their level of commitment to it. If, solely on the basis of their responses, the manager reaches several conclusions

on how employee commitment can be increased, the whole approach to the investigation is unscientific. It lacks rigor for the following reasons:

1. The conclusions are incorrectly drawn because they are based on the responses of just a few employees whose opinions may not be

representative of those of the entire workforce.

2. The manner of framing and addressing the questions could have introduced bias or incorrectness in the responses.

3. There might be many other important influences on organizational commitment that this small sample of respondents did not or could no

verbalize during the interviews, and the researcher has therefore failed to include them.

Therefore, conclusions drawn from an investigation that lacks a good theoretical foundation, as evidenced by reason 3, and methodologicasophistication, as evident from 1 and 2 above, are unscientific. Rigorous research involves a good theoretical base and a carefully thought-ou

methodology. These factors enable the researcher to collect the right kind of information from an appropriate sample with the minimum degree of

bias, and facilitate suitable analysis of the data gathered. The following chapters of this book address these theoretical and methodological issues

Rigor in research design also makes possible the achievement of the other six hallmarks of science that we shall now discuss.

Testabilily

If, after talking to a random selection of employees of the organization and study of the previous research done in the area of organizational

commitment, the manager or researcher develops certain hypotheses on how employee commitment can be enhanced, then these can be tested

by applying certain statistical tests to the data collected for the purpose. For instance, the researcher might hypothesize that those employees who

perceive greater opportunities for participation in decision making will have a higher level of commitment. This is a hypothesis that can be tested

when the data are collected. A correlation analysis will indicate whether the hypothesis is substantiated or not. The use of several other tests, such

as the chi-square test and the ttest, is discussed in Chapters ll and 12. Scientific research thus lends itself to testing logically developed

hypotheses to see whether or not the data support the educated conjectures or hypotheses that are developed after a careful study of the

problem situation. Testability thus becomes another hallmark of scientific research.

ReplicabilityLet us suppose that the manager/ researcher, based on the results of the study, concludes that participation in decision making is one of the mos

important factors that influences the commitment of employees to the organization. We will place more faith and credence in these findings and

conclusion if similar findings emerge on the basis of data collected by other organizations employing the same methods. To put it differently, the

results of the tests of hypotheses should be supported again and yet again when the same type of research is repeated in other similar

circumstances. To the extent that this does happen (i.e., the results are replicated or repeated), we will gain confidence in the scientific nature o

our research. In other words, our hypotheses have not been supported merely by chance, but are reflective of the true state of affairs in the

population. Replicability is thus another hallmark of scientific research.

Precision and confidence

In management research, we seldom have the luxury of being able to draw "definitive" conclusions on the basis of the results of data analysis. This

is because we are unable to study the universe of items, events, or population we are interested in, and have to base our findings on a sample that

we draw from the universe. In all probability, the sample in question may not reflect the exact characteristics of the phenomenon we are trying to

study (these difficulties are discussed in greater detail in Chapter 10). Measurement errors and other problems are also bound to introduce an

element of bias or error in our findings. However, we would like to design the research in a manner that ensures that our findings are as close to

reality (i.e., the true state of affairs in the universe) as possible, so that we can place reliance or confidence in the results.Precision refers to the closeness of the findings to "reality" based on a sample. In other words, precision reflects the degree of accuracy o

exactitude of the results on the basis of the sample, to what really exists in the universe. For example, if I estimated the number of production days

lost during the year due to absenteeism at between 30 and 40, as against the actual figure of 35, the precision of my estimation compares more

favorably than if I had indicated that the loss of production days was somewhere between 20 and 50. You may recall the term confidence interva

in statistics, which is what is referred to here as precision. Confidence refers to the probability that our estimations are correct. That is, it is not

merely enough to be precise, but it is also important that we can confidently claim that 95% of the time our results will be true and there is only a

5% chance of our being wrong. This is also known as the confidence level.

The narrower the limits within which we can estimate the range of our predictions (i.e., the more precise our findings) and the greater the

confidence we have in our researchresults, the more useful and scientific the findings become. ln social science research, a 95% confidence level

which implies that there is only a 5% probability that the findings may nut be correct - is accepted as conventional, and is usually referred to as a


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significance level of0.05 (p == 0.05). Thus, precision and confidence are important aspects of research, which areattained through appropriate

scientific sampling design. The greater the precision and confidence we aim at in our research, the more scientific is the investigation and the more

useful are the results. Both precision and confidence are discussed in detail in Chapter 10 on Sampling.

Objectivity

The conclusions drawn through the interpretation of the results of data analysis should be objective; that is, they should be based on the facts of

the findings derived from actual data, and not on our own subjective or emotional values. For instance, if we had a hypothesis that stated tha

greater participation in decision making would increase organizational commitment, and this was not supported by the results, it would make no

sense if the researcher continued to argue that increased opportunities for employee participation would still help!

Such an argument would be based, not on the factual, data-based research findings, but on thesubjective opinion of the researcher. If this was the

researcher's conviction all along, then there was no need to do the research in the first place! Much damage can be sustained by organizations that

implement non-data-based or misleading conclusions drawn from research. For example, if the hypothesis relating to organizational commitmentin our previous example was not supported, considerable time and effort would be wasted in finding ways to create opportunities for employee

participation in decision making. We would only find out later that employees still kept quitting, remained absent, and did not develop any sense o

commitment to the organization. Likewise, if research shows that increased pay is not going to increase the job satisfaction of employees, then

implementing a revised, increased pay system will only drag down the company financially without attaining the desired objective. Such a futile

exercise, then, is based on nonscientific interpretation and implementation of the research results. The more objective the interpretation of the

data, the more scientific the research investigation becomes. Though managers or researchers might start with some initial subjective values and

beliefs, their interpretation of the data should be stripped of personal values and bias. If managers attempt to do their own research, they should

be particularly sensitive to this aspect. Objectivity is thus another hallmark of scientific investigation.

Generalizability

Generalizabilityrefers to the scope of applicability of the research findings in one organizational setting to other settings. Obviously, the wider the

range of applicability of the solutions generated by research, the more useful the research is to the users. For instance, if a researchers finding

that participation in decision making enhances organizational commitment are found to be true in a variety of manufacturing, industrial, and

service organizations, and not merely in the particular organization studied by the researcher, then the generalizability of the findings t o othe

organizational settings is enhanced. The more generalizable the research, the greater its usefulness and value. However, not many researchfindings can be generalized to all other settings, situations, or organizations. For wider generalizability, the research sampling design has to be

logically developed and a number of other details in the data~collection methods need to be meticulously followed.

However, a more elaborate sampling design, which would doubtless increase the generalizability of the results, would also increase the costs of

research. Most applied research is generally confined to research within the particular organization where the problem arises, and the results, a

best, are generalizable only to other identical situations and settings. Though such limited applicability does not necessarily decrease its scientific

value (subject to proper research), its generalizability is restricted.

Parsimony

Simplicity in explaining the phenomena or problems that occur, and in generating solutions for the problems, is always preferred to complex

research frameworks that consider an unmanageable number of factors. For instance, if two or three specific variables in the work situation are

identified, which when changed would raise the organizational commitment of the employees by 45%, that would be more useful and valuable to

the manager than if it were recommended that he should change ten different variables to increase organizational commitment by 48%. Such an

unmanageable number of variables might well be totally beyond the manager's control to change. Therefore, the achievement of a meaningful and

parsimonious, rather than an elaborate and cumbersome, model for problem solution becomes a critical issue in research.

Economy in research models is achieved when we can build into our research framework a lesser number of variables that explain the variance farmore efficiently than a complex set of variables that only marginally add to the variance explained. Parsimony can be introduced with a good

understanding of the problem and the important factors that influence it. Such a good conceptual theoretical model can be realized through

unstructured and structured interviews with the concerned people, and a thorough literature review of the previous research work in the particula

problem area. In sum, scientific research encompasses the eight criteria just discussed. These are discussed in more detail later in this book. At this

point, a question that might be asked is why a scientific approach is necessary for investigations when systematic research by simply collecting and

analyzing data would produce results that could be applied to solve the problem. The reason for following a scientific method is that the results wil

be less prone to error and more confidence can be placed in the findings because of the greater rigor inapplication of the design details. This also

increases the replicability and generalizability of the findings.

The hypothetico-deductwe method

Scientific research pursues a step-by-step, logical, organized, and rigorous method (a scientific method) to find a solution to a problem. The

hypothetico-deductive method, popularized by the Austrian philosopher Karl Popper, is a typical version of the scientific method. The hypothetico

deductive method provides a useful, systematic approach to solving basic and managerial problems. This systematic approach is discussed next.

The seven-step process in the hypothetico-deductive method

1. Identify a broad problem area

2. Define the problem statement

3. Develop hypotheses

4. Determine measures

5. Data collection

6. Data analysis

7. Interpretation of data

Identity a broad problem area

A drop in sales, frequent production interruptions, incorrect accounting results, low-yielding investments, disinterestedness of employees in their

work, customer switching, and the like, could attract the attention of the manager and catalyze the research project.

Define the problem statement

Scientific research starts with a definite aim or purpose. To find solutions for identified problems, a problem statement that states the genera

objective of the research should be developed. Gathering initial information about the factors that are possibly related to the problem will help us


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to narrow the broad problem area and to define the problem statement. Preliminary information gathering, discussed in greater detail in Chapter

3, involves the seeking of information in depth, of what is observed (for instance the observation that our company is losing customers). This could

be done by a literature review (literature on customer switching) or by talking to several people in the work setting, to clients (why do they

switch?), or to other relevant sources, thereby gathering information on what is happening and why. Through any of these methods, we get an idea

or a "feel" for what is transpiring in the situation. This allows us to develop a specificproblem statement.

Develop hypotheses

In this step variables are examined as to their contribution m' influence in explaining why the problem occurs and how it can be solved. The

network of associations identified among the variables is then theoretically woven, together with justification as to why they might influence the

problem. From a theorized network of associations among the variables, certain hypotheses or educated conjectures can be generated. Fo

instance, at this point, we might hypothesize that specific factors such as overpricing, competition, inconvenience, and un -responsive employee

affect customer switching. A scientific hypothesis must meet two requirements. The first criterion is that the hypothesis must be testable. A famousexample of a hypothesis that is not testable is the hypothesis that God created the earth. The second criterion, and one of the central tenets of

the hypothetico-deductive method, is that a hypothesis must also be falsifiable. That is, it must be possible to disprove the hypothesis. According to

Karl Popper, this is important because a hypothesis cannot be confirmed; thereis always a possibility that future research will show that it is false.

Hence, failing to falsify (E) a hypothesis does not prove that hypothesis: it remains provisional until it is disproved. Hence, the requirement of

falsifiability emphasizes the tentative nature of research findings: we can only prove" our hypotheses until they aredisproved. The developmen

of hypotheses and the process of theory formulation are discussed in greater detail in Chapter 4.

Determine measures

Unless the variables in the theoretical framework are measured in some way, we will not be able to test our hypotheses. To test the hypothesis

that unresponsive employees affect customer switching, we need to operatianalize unresponsiveness and customer switching. Measurement of

variables is discussed in Chapters 6 and 7.

Data collection

After we have determined how to measure our variables, data with respect to each variable in the hypothesis need to be obtained. These data then

form the basis for data analysis. Data collection is extensively discussed in Chapters 5, 8, 9, and 10.

Data analysisIn the data analysis step, the data gathered are statistically analyzed to see if the hypotheses that were generated have been supported. Fo

instance, to see if unresponsiveness of employees affects customer switching, we might want to do a correlational analysis to determine the

relationship between these variables. Hypotheses are tested through appropriate statistical analysis, discussed in Chapter 12. Analyses of both

quantitative and qualitative data can be done to determine if certain conjectures are substantiated. Qualitative data refer to information gathered

in a narrative form through interviews and observations. For example, to test the theory that budgetary constraints adversel y impact on managers

responses to their work, several interviews might be conducted with managers after budget restrictions are imposed. The responses from the

managers, who verbalize their reactions in different ways, might then be organized to see the different categories under which they fall and the

extent to which the same kinds of responses are articulated by the managers.

Interpretation of data

Now we must decide whether our hypotheses are supported or not by interpreting the meaning of the results of the data analysis. For instance, if it

was found from the data analysis that increased responsiveness of employees was negatively related to customer switching (say, 0.3), then we can

deduce that if customer retention is to be increased, our employees have to be trained to be more responsive. Another inference from this data

analysis is that responsiveness of our employees accounts for (or explains) 9% of the variance in customer switching (0.32). Based on these

deductions, we are able to make recommendations on how the customer switching" problem may be solved (at least to some extent); we have totrain our employees to be more flexible and communicative.

Note that even if the hypothesis on the effect of unresponsiveness on customer switching is not supported, our research effort has still been

worthwhile. Hypotheses that are not supported allow us to refine our theory by thinking -about why it is that they were not supported. We can

then test our refined theory in future research.

Review of the hypothetico-deductive method

The hypothetico-deductive method involves the seven steps of identifying a broad problem area, defining the problem statement, hypothesizing

determining measures, data collection, data analysis, and the interpretation of the results. Deductive reasoning is a key element in the

hypothetico-deductive method. In deductive reasoning, we start with a general theory and then apply this theory to a specific case.

Hypothesis testing is deductive in nature because we test if a general theory (for instance the theory that "customer satisfaction is based on the

service quality dimensions of responsiveness, reliability, assurance, tangibles, and empathy") is capable of explaining a particular problem; the

problem that catalyzed our research project (for instance, complaints about the service quality our company provides). Hence, service quality

theory is used to make predictions about relationships between certain variables in our specific situation; for instance, that there is a positive

relationship between perceived employee responsiveness and satisfaction of our customers. ht a similar vein, marketing researchers often deducethe consequences of changes in the marketing mix based on existing (marketing) models.

Inductive reasoning works in the opposite direction: it is a process where we observe specific phenomena and on this basis arrive at genera

conclusions. Along these lines, the observation of a first, second, and third white swan may lead to the proposition that "all swans are white. In

this example, the repeated observation of a white swan has led to the conclusion that all swans are white. According to Karl Popper it is not

possible to "prove" a hypothesis by means of induction, because no amount of evidence assures us that contrary evidence will not be found.

Observing 3, 10, 100, or even 10 000 white swans, does not justify the conclusion that all swans are white" because there is always a possibility

that the next swan we will observe will be black. Instead, Popper proposed that science is accomplished by deduction.

However, despite Popperscriticism on induction, both inductive and deductive processes are often used in research. Indeed, many researchers

have argued that both theory generation (induction) and theory testing (deduction) are essential parts of the research process.

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