Decision making is crucial for making appropriate choices and thereby ascertain the success of any given business. In the current marketplace, decision making has given immensely challenging considering the number of variables to be considered and the underlying uncertainty. As a result, there is a significant role of various quantitative methods which enable objective evaluation of the available choices which can improve the overall decision making. The objective of the given report is to present the various steps involved in problem solving or decision making. Further, using the process as a base, the relationship between the quantitative methods and decision making would be highlighting using a case study as an example.
The process of solving a particular problem at hand or making a decision is referred to as problem solving or decision making. There are mainly four steps or processes that are involved in decision making. The first step is recognition of problem i.e. the concerned manager or decision maker must realise that there is a problem which needs to be solved or a situation where a decision needs to be made. This is imperative because once the concerned person realises the presence of problem would there be steps to resolve the same. Usually the problem is recognised when the performance or output is not as desired (Eriksson & Kovalainen, 2015).
The next step is decision making is to search for various alternatives that could potentially resolve the situation or problem at hand. Thus, based on the nature of the problem or decision, the decision maker or manager would take requisite assistance so as to narrow down on the available courses of action. For a routine problem, identification of the alternatives is rather easy but for a complex problem, even searching for the alternatives could be an arduous task which may require reference to the available literature. Once the alternatives have been identified by the decision making, the next step commences which requires rational evaluation of the available options or alternatives so that the best alternative may be chosen for the problem at hand (Flick, 2015).
For programmed decisions, this is quite easy as there are established procedures along with available tools to evaluate the alternatives available. However, the same is not true for non-programmed decisions as enough information is not available or the same is not reliable due to which rational decision making or evaluation is hampered. Once the evaluation of alternatives is done, then a decision is taken and implementation is done. This usually involves putting the decision taken into implementation. This is followed by feedback so as to ensure that the desired output or outcome is achieved failing which rectification needs to be done (Hair et. al., 2015).
Quantitative methods may be defined as those computation techniques which tend to emphasize on data collection through various means and carrying out the numerical or statistical analysis of the same. These methods are critical for taking sound managerial decisions. This is primarily because the results obtained from these methods are objective and reliable. Further, the quantitative methods if implemented appropriately could potentially improve the quality of the managerial decisions made as they would be more rational considering the availability of strong evidence in the form of numerical analysis. The quantitative methods tend to identify the various relationships that tend to exist between the various variables present in the available data and thereby enable the decision maker to base the decision based on these underlying patterns. Examples of quantitative methods tend to include various statistical techniques such as hypothesis testing, regression analysis, correlation analysis and other descriptive statistical techniques (Hillier, 2006).
One of the key steps which is involved in the decision making process is the evaluation of alternatives. In this process, the various quantitative methods are quite useful. This is because they enable the decision maker to recognise patterns of association and relationship between the variables of interest and hence provide useful information in the form of expected outcome provided a given decision is taken. A case in point could relate to whether an incremental expenditure on advertisement must be done or not. In order to evaluate the proposal, the impact of advertisement on sales needs to be understood using the historical data through the usage of correlation or regression analysis. This would allow the decision maker to work out whether the respective benefit likely to arise in the form of sales would outweigh the expected incremental cost (Hastie, Tibshirani & Friedman, 2011).
While the above is relatively a straight forward example, the quantitative methods could be used for more complex analysis particularly for estimating the likely scenarios and the possible outcome in each of these. Then probabilities could be accorded to these scenarios based on their respective chance of happening. On the basis of this analysis, it would be possible to estimate the likely payoff associated with the various alternatives that the manager or respective decision maker might have identified. Further, since these quantitative methods are based on data, hence the output thus obtained has higher reliability as the same can be easily verified. Thus, the use of quantitative methods play a significant role in the decision making process at the corporate level (Flick, 2015).
A real life case study has been obtained from a Journal named Journal of Political Economy where an article named economic effects of broadcast licensing was published in 1964 by H, Levin. The quantitative method which had been used by the author was regression analysis. Based on the sales transactions of 31 radio stations, the data regarding the independent variables and dependent variable (price) was obtained. The various independent variables outlined in the case are highlighted below (Levin, 1964).
The above output highlights various regression models with price acting as the dependent variable and different variables acting as the independent variables. There are three different regression models with one model using only spotrate with the other adding income also and the third adding networks also. Using the significance values in the last column, a decision maker can narrow down on the respective independent variables that should be considered important while predicting the price. For instance, for a significance level of 5%, the networks variable does not seem significant as the p value (0.147) exceeds the significance level. Hence, the model 2 would be termed as the most superior which is also established from the fact that it leads to the lowest standard error. Hence, the decision maker would use the given model for estimation of a reasonable price (Flick, 2015).
Based on the above discussion, it is apparent that decision making has various stages and increasingly the evaluation of alternatives is getting more challenging. In the light of the same, quantitative methods serve critical role by providing key support to the decision maker in relation to evaluating the available alternatives. This has also been highlighted using an actual case study whereby the price of the radio station has been predicted based on the empirical data available in this regards.
Eriksson, P. & Kovalainen, A. (2015). Quantitative methods in business research (3rd ed.). London: Sage Publications.
Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research project (4th ed.). New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of business research methods (2nd ed.). New York: Routledge.
Hastie, T., Tibshirani, R. & Friedman, J. (2011). The Elements of Statistical Learning (4th ed.). New York: Springer Publications.
Hillier, F. (2006). Introduction to Operations Research. (6th ed.). New York: McGraw Hill Publications.
Levin, H. (1964). Economic Effects of Broadcast Licensing, Journal of Political Economy, 72(4), 152-162.
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