Quantitative Research Design and Application in Business

 In quantitative research your aim is to determine the relationship between one thing (an independent variable) and another (a dependent variable) in a population. Quantitative research designs are either descriptive (subjects usually measured once) or experimental (subjects measured before and after a treatment). A descriptive study establishes only associations between variables. An experiment establishes causality.For an accurate estimate of the relationship between variables, a descriptive study usually needs a sample of hundreds or even thousands of subjects; an experiment, especially a crossover, may need only tens of subjects. The estimate of the relationship is less likely to be biased if you have a high participation rate in a sample selected randomly from a population. In experiments, bias is also less likely if subjects are randomly assigned to treatments, and if subjects and researchers are blind to the identity of the treatments. In all studies, subject characteristics can affect the relationship you are investigating. Limit their effect either by using a less heterogeneous sample of subjects or preferably by measuring the characteristics and including them in the analysis. In an experiment, try to measure variables that might explain the mechanism of the treatment. In an unblinded experiment, 

Business Schools have recently been attempting to bridge the gap between academic course offerings and the real demands of the decision-making manager. One of the greatest challenges concerns the area of management science, or quantitative business methods. From the beginning, managers have questioned the applications of quantitative methods in business.

Management scientists are concerned that there is a "practicality gap" between quantitative business methods and management. Managers claim they are not using models because they neither adequately represent the true complexity of situations nor meet real-world needs. The educational process has been criticized for placing too much emphasis on theory and for ignoring the needs of the practitioner. In other words, the educational process with respect to quantitative business techniques is "user-deficient." The divergence of views on the relative importance of theory versus application continues to be a dividing line between academicians and managers. Managers' contention is that students leave schools trained in techniques but with little or no exposure to real problems. Educators have countered that the job of the university is to educate people, and this should involve more than vocational training for particular jobs in industry.

Some progress is being made, however, in larger organizations. In general, as gross sales of an organization increase, the applications of quantitative business methods also increase. This positive correlation can also be noted for increases in the number of employees in an organization.

In order to overcome the ineffectiveness of operations research techniques, management scientists must learn to think like managers.

The most significant barriers cited were related to lack of knowledge. In order to overcome these barriers to user effectiveness ,there are proposed  number of suggestions, including:

1.Those with quantitative skills within the company should direct considerably more of their time toward educating managers.

2. Quantitative training needs to play a larger role in executive development programs.

This lends support to the long-held belief about the relationship between knowledge and use. Findings from other studies have also indicated that increasing manager knowledge is an effective means of improving technique utilization.

In addition to the descriptive illustration presented above, a number of statistical tests were performed. The first test concerned the following hypothesis:

 3.The application of OR techniques is independent of the firm's main business activity.

The business activities defined were manufacturing, marketing, distribution, and other. The tests indicated that type of business activity is not related to the utilization of OR methods. The second hypothesis tested was as follows:

 4.The application of OR techniques is independent of the firm's level of sales.

The tests revealed that there is a relationship between level of sales and utilization of OR techniques. Further examination of the data implied that firms with higher levels of sales tend to use OR techniques more often than firms with lower sales levels. Finally, a third hypothesis was tested:

Financial managers use sales forecasts to plan their cash and borrowing positions during the year. Management requires forecasts for planning capital expenditures of new plant and equipment, for planning major promotional activities, and for planning the general direction and future course of the firm. The results presented in Table 4 support the notion that the need for forecasts cuts across all functional lines.

Table 5 summarizes responsibility for forecasting applications by an individual's functioning area. Note that most of the forecasting projects are performed by marketing and management personnel. In contrast to OR users, industrial engineers and operations researchers are not heavily involved in forecasting applications. Some respondents indicated that marketing was more closely involved with forecasting because "it has direct responsibility for providing sales forecasts of future levels of demand," or "Industrial engineers and operations researchers have the knowledge but are less likely to be a part of the forecasting process."

Table 6 compares the use of nine forecasting techniques. This table is analogous to Table 3 presented earlier. The data reveal that trend analysis, seasonal-cyclical indexes, and moving averages tend to be the most frequently used, while Box-Jenkins analysis is almost never used. The respondents gave the same reasons for not using these methods as they did for not using OR techniques: they perceived no need for them, and they lacked the skills and resources necessary to use them. 

The most common remedy put forth by respondents was gain increased training. However, a word of caution is necessary here. Increasing the forecaster's knowledge of sophisticated methods may not necessarily lead to improved performance. Rather, the training should instruct users of forecasts in the pros and cons of alternative methods and in the identification of situations where forecasting can play a major role in improving organizational decision making. Finally, a forecaster's position should have the requisite authority and responsibility to ensure that forecasting is performed at a level in the organization that will allow its proper impact on decision-making.

The three hypotheses examined for the OR techniques were also tested for forecasting; these tests yielded results analogous to those for OR techniques.

 References

Samuel Eilon, "Mathematical Modeling for Management," Interfaces, February 1974, pp. 32-38.

James R. Emshoff, "Experience-Generalized Decision Making: The Next Generation of Managerial Models," Interfaces, August 1978, pp. 40-48.

Robert J. Graham, "is Management Science Arcane?" Interfaces, February 1977, pp. 63-67.

T.B. Green, W.B. Newsom, and S.R. Jones, "A Survey of the Application of Quantitative Techniques to Production/Operations Management in Large Corporations," Academy of Management Journal, 20, 4 (1977): 669-676.

William H. Gruber and John S. Niles, "Problems in the Utilization of Management Science/Operations Research: A State of the Art Survey," Interfaces, 2, 1 (1971: 12-19.

Rick Hesse, "Management Science or Management/Science?" Interfaces, February 1980, pp. 104-104.

Ronald A. Howard "The Practicality Gap," Management Science, March 1968, pp. 503-507.