Successful managerial decisions are seldom made on hunches or on trail and error method. The sound and effective decisions are always made on the basis of scientific research. Scientific research focuses on solving problems in a step _by _step logical, organized and rigorous manner in each step of research viz., identifying problem, gathering data, analyzing it and in arriving at a valid conclusion. Organizations may not always be involved in the scientific research due to various reasons like – simple problems which can solved with previous experience, time contingency, lack of knowledge, resource constraints etc., However the scientific research performed in a rigorous and systematic way leads to repeatable and comparable research findings. It also enables the researchers to arrive at accurate, dependable and subjective findings. The hallmarks or distinguishing characteristic features of scientific research are as follows:
The research is conducted with a purpose. It has a focus. The purpose of the research should be clearly mentioned in an understandable and unambiguous manner. The statement of the decision problem should include its scope, its limitations and the precise meaning of all words and terms significant to the research. Failure to mention the purpose clearly will raise doubts in the minds of stakeholders of the research as to whether the researcher has sufficient understanding of the problem.
Rigor means carefulness, scrupulousness and the degree of exactness in research investigation. In order to make a meaningful and worthwhile contribution to the field of knowledge, research must be carried out rigorously. Conducting a rigorous research requires a good theoretical knowledge and a clearly laid out methodology. This will eliminate the bias; facilitate proper data collection and analysis, which in turn would lead to sound and reliable research findings.
Research should be based on testable assumptions/hypotheses developed after a careful study of the problems involved. The scientific research should enable the testing of logically developed hypotheses to see whether or not the data collected support the hypotheses developed.
Research findings would command more faith and credence if the same results are evolved on different set of data. The results of the test hypothesis should be supported again and again when the same type of research is repeated in other similar circumstances. This will ensures the scientific nature of the research conducted and more confidence could be placed in the research findings. It also eliminates the doubt that the hypotheses are supported by chance and ensures that the findings reflect the true state of affairs.
Precision and Confidence
In management research the findings are seldom definitive due to the fact that the universe of items, events or population are not taken as such but based on sample drawn from universe. There is a probability that the sample may not reflect the universe. Measurement errors and other problems are bound to introduce an element of error in the findings. However the research design should ensure that the findings are as close to the reality as possible so that one can have confidence in the findings.
Precision refers to the closeness of the finding to ‘reality’ based on sample. It reflects the degree of accuracy or exactitude of the results on the basis of the sample to what exactly is in the universe. The confidence interval in statistics is referred here as precision.
Confidence refers to the probability that the estimation made in the research findings are correct. It is not enough if the results are precise but it is also important to claim that 95% of the time the results would be true and there is only a 5% chance of the results being wrong. This is known as confidence level. If the precision and confidence levels of the research findings are higher then the findings of the research study would be more scientific and useful. Precision and confidence can be attained through appropriate scientific sampling design.
Research finding should be factual, databased and free from bias. The conclusion drawn should be based on the facts of the findings derived form the actual data and not on the basis of subjective or emotional values. Business organizations will suffer a greater extent of damage if a non-data-based or misleading conclusion drawn from the research is implemented. Scientific approach ensures objectivity of research.
It refers to the scope of applying the research findings of one organizational setting to other settings of almost similar nature. The research will be more useful if the solutions are applicable to a wider range. The more generlizable the research, the greater will be its usefulness and value. However it is not always possible to generalize the research findings to all other settings, situations or organizations. For achieving genaralizability the sampling design has to be logically developed and data collection method needs to be very sound. This may increase the cost of conducting the research. In most of the cases though the research findings would be based on scientific methods it is applicable only to a particular organization, settings or situations.
Research needs to be conducted in a parsimonious i.e. simple and economical manner. Simplicity in explaining the problems and generalizing solutions for the problems is preferred to a complex research framework. Economy in research models can be achieved by way of considering less number of variables leading to greater variance rather than considering more number of variables leading to less variance. Clear understanding regarding the problem and the factors influencing the same will lead to parsimony in research activities. The sound understanding can be achieved through structured and unstructured interview with the concerned people and by undertaking a study of related literature in the problem area.
The scientific research in management area cannot fulfill all the above-discussed hallmarks to the fullest extent. In management research it is not always possible to conduct investigations that are 100% scientific like in physical science as it is difficult to collect and measure the data regarding the feelings, emotions, attitudes and perception. It is also difficult to obtain representative sample; these aspects restrict the generlizability of the findings. Though it is not possible to meet all the above said characteristics of the scientific research, to the extent possible the research activities should be pursued in the scientific manner.
Reason is the tool by which the human mind comes to understand the world. There are two processes by which reason tries to understand events: deductive reasoning, based on generally accepted principles, and inductive reasoning, in which general principles are formed from observed events. The field of economics has deductive and inductive sides, which are complementary to each other.
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Deduction in Economics
• Deductive economics starts with a set of axioms about economies and how they work, and relies on these principles to explain individual cases or events. Supply and demand analysis, a staple in any introductory economics course, is an example of deductive reasoning because it involves a set of generally accepted principles about demand and supply. To summarize, deduction in economics starts with a generally accepted principle and proceeds to the specific. Induction in Economics
• Inductive reasoning in economics does the reverse of deductive reasoning; namely, it begins with an individual problem or question and proceeds to form a general principle based on the evidence observed in the real world of economic activity. For example, an economist who asks if a government program of public works spending will stimulate a region’s economy will proceed to research the issue, collect and analyze data, and based on conclusions, form a general theory about the economic impact of fiscal policies.
Read more: Induction Vs. Deduction Economics | eHow.com http://www.ehow.com/about_5427109_induction-vs-deduction-economics.html#ixzz2KoGFvGGU
• Although deduction and induction represent two differing approaches to understanding economic phenomena, the 19th century American economist Henry George observed that they are related. George noted that induction involves the use of human reason to investigate facts, while deduction is the derivative of the former.
• Applying George’s insight on deduction and induction in economics, deduction involves the use of economic principles and theories that have been empirically verified through observation, research, and critical analysis. Generally accepted principles of supply and demand, for example, can inform our understanding of economic transactions only if they are based on empirical evidence, collected and analyzed through the inductive process.
• Induction in economics requires rigorous use of the methodology of economic research. This includes use of the mathematical modeling and statistical processes used in econometrics, or economic measurement. Findings from inductive reasoning then form economic theories used in deductive analysis.
Read more: Induction Vs. Deduction Economics | eHow.com http://www.ehow.com/about_5427109_induction-vs-deduction-economics.html#ixzz2KoGNpTEs
Logical arguments are usually classified as either ‘deductive’ or ‘inductive’. Deduction: In the process of deduction, you begin with some statements, called ‘premises’, that are assumed to be true, you then determine what else would have to be true if the premises are true. For example, you can begin by assuming that God exists, and is good, and then determine what would logically follow from such an assumption. You can begin by assuming that if you think, then you must exist, and work from there. In mathematics you can begin with some axioms and then determine what you can prove to be true given those axioms. With deduction you can provide absolute proof of your conclusions, given that your premises are correct. The premises themselves, however, remain unproven and unprovable, they must be accepted on face value, or by faith, or for the purpose of exploration.
Induction: In the process of induction, you begin with some data, and then determine what general conclusion(s) can logically be derived from those data. In other words, you determine what theory or theories could explain the data. For example, you note that the probability of becoming schizophrenic is greatly increased if at least one parent is schizophrenic, and from that you conclude that schizophrenia may be inherited. That is certainly a reasonable hypothesis given the data. Note, however, that induction does not prove that the theory is correct. There are often alternative theories that are also supported by the data.
For example, the behavior of the schizophrenic parent may cause the child to be schizophrenic, not the genes. What is important in induction is that the theory does indeed offer a logical explanation of the data. To conclude that the parents have no effect on the schizophrenia of the children is not supportable given the data, and would not be a logical conclusion. Deduction and induction by themselves are inadequate for a scientific approach. While deduction gives absolute proof, it never makes contact with the real world, there is no place for observation or experimentation, no way to test the validity of the premises. And, while induction is driven by observation, it never approaches actual proof of a theory. The development of the scientific method involved a gradual synthesis of these two logical approaches.
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