Quantitative Management Research for Quality


Quantitative management is also referred to as operations research. It is a systematic and scientific approach to problem-solving and decision-making processes. This is especially in complex situations that are replete with many uncertainties. The environment where the method is applied is not only complex, but also is full of conflicts, and as such, a scientific approach is called for. Quantitative management aims to come up with the best solution to a crisis or any situation regarded as a problem by the manager. Quantitative management holds that the best solution can only be derived from quantitative mathematical models, which clarify the problem by enabling the manager to alter some of the variables. The alteration of the variables in the model provides the manager with a myriad of probable solutions, from which they can choose the best.

Quantitative management can be applied in a wide range of fields. It has been used in strategic management of military maneuvers, in production, and in other places that critical decisions are called for. What is needed for quantitative management to take effect is the presence of numerical data, since the managers rely on this data to come up with a model of multiple variables.

Today’s manager operates within an environment characterized by many forms of varying competition. There is competition for clients, running to beat deadlines, and such other forms of demanding situations calling for critical decisions to be made. This has necessitated the process of refining the models that the managers use to come up with solutions. Research is one tool that is utilized to refine and hone the effectiveness of decision-making models in quantitative management. A case in point is a manager who needs to decide on the placement of a certain product in the market. To come up with an effective model to make the decision, market research is needed.

It is in this light that it becomes very important to focus on quantitative management research. For useful models to be developed there is a need to improve the quality and impact of quantitative management research. For example, it is crucial to come up with a model that applies theoretical frameworks o real-life situations. This can only be achieved after research has been conducted that comes up with the best connection between the two.

Objectives of the Study

The major objective of this study is to examine strategies that can be used to improve the quality and impact of quantitative management research. To achieve this objective, the writer will be guided by several specific objectives. These areas are listed below:

  1. Quantitative management: Historical Background
  2. Major problems in quantitative research methodology
  3. Probable solutions for the problems in (2) above
  4. Importance of improving quality and impact of quantitative management research

Quantitative Management: Historical Background

Before embarking on strategies that are used to improve quantitative management research, it is important to give a brief historical background on the same. This is the only way that we can appreciate the importance of improving the quality and impact of quantitative research methods. In addition, the discourse that will follow will be contextualized in this historical background.

Quantitative management techniques can be traced back to the era of World War II (Faboxxi, Focardi & Jonas 37). When the war broke out, new problems were presented to military strategists that needed to be solved from a relatively new perspective. This is given the fact that new war techniques, which were hitherto unknown or inexperienced by the strategists, were introduced. This is for example submarines and massive airstrikes. War became a complex undertaking, and errors became costly overnight. There was simply no room for error, and this called for new management techniques of the whole process.

Britain realized that unless something drastic was done, the war will be lost. This formed the impetus to create an operations research team (Bertrand & Fransoo 241). It was made up of mathematicians, statisticians, physicists, and an array of other professional experts. The aim was to come up with a strategy to counter Germany’s incursion. Sophisticated mathematical models and formulas were developed. Attack and counterattack scenarios were reduced to mathematical models, and commanders were able to alter the variables in the model to come up with an effective strategy for each situation. From this effort, military errors were greatly reduced, and this is believed to be the reason why Britain was able to withstand the German onslaught (Hahn 2).

After the war ended, the success of this strategy attracted industrialists, who needed to come up with models to make decisions in the industrializing society. Improvements in computer technology made it possible for managers to come up with ever-complicated and sophisticated models to address ever-complicated scenarios (Hahn 3). Production managers especially found the models to be so effective in solving problems along their professional lines. For example, a manager may wish to know what kind of effect workers’ strikes will have on production. He will come up with a model and change this variable by different degrees to come up with different scenarios. From this, the manager can come up with informed decisions on strategies that will be used to counteract such varying problems should they arise in the future.

Major Problems in Quantitative Management Research Methodology

Deduction and induction are the two major logical reasonings applied in any form of social research. The former involves transiting from generalizations or theories to the specific situation, while the latter is made up of transition from specifics to generalizations. Quantitative management utilizes deductive reasoning. This means that the problems that plague any form of deductive research can also inflict quantitative management research methodologies.

Poorly-Defined Variable Measurement

Echambachi, Campbell & Rajshree are of the view that if a variable measurement tool is constructed from ill-informed reasonings, the result is a “poorly tested theory” (1809). Variables are the building blocks creating the crucial bridge between theoretical reasoning and testing of the same. As such, if the variable is not measured properly, the results will be misleading since the theory would not have been tested effectively. This, according to Echambachi et al, is a serious threat to the process of management research (1809).

If an error occurs in the measurement of a single dependent variable during the research, one or all of the other variables are affected. This leads to type I and types II errors in the process of testing a hypothesis (Echambachi et al 1801). Take a case of market research where variables include prices of competitors and cost of production. If one of the variables-say costs of production-is wrongly measured, the manager might be misled to price the product below the competitor’s range, while at the same time, unwittingly setting it below the cost of production. This will reduce the profit margin accrued by the company.

Correction of Measurement Errors

Measurement errors can be addressed from two different pedestals. These are psychometric and econometric perspectives (Loomba 5). The latter helps in correcting the errors through ex-ante accommodation in the process of primary data collection (Lee 212). The latter accommodates ex-post correction of the error in secondary data (Lee 212).

Ex-Ante Accommodation

This is one of the techniques used to improve the quality and impact of quantitative management research through the correction of variable measurement errors. It is used in the primary data collection process. In this process, the researcher needs to ensure that validity and reliability are ensured. This can be achieved by defining the variables explicitly, defining the limits and boundaries of each (Echambachi et al 1810). From this, the tool that will be used to measure the variable can be applied within well-defined boundaries, improving accuracy and validity.

To avert type II errors, quantitative management researchers should utilize multiple items and variables, as opposed to using singles (Faboxxi et al 38). This way, it becomes possible to explore the extremes of the fields addressed by the variables. The variables should also be pretested, for example through pilot studies, before embarking on data collection in the field. It is from this that they can be refined and their accuracy enhanced. If the researcher opts to use multiple items on the same variable, they must address the issue of relationship. For example, how is the item related to the variable, and how is it in turn tied to the other items?

Take for example a military strategist who desires to come up with a strategy that will counter airstrikes from an enemy. The variables, in this case, may be the planes that will be used and the form of weapons that are likely to be deployed by the enemy. Taking the variable of attack planes, items to measure this variable can be the number of planes, the models, and their power of the attack. The strategist needs to decipher the relationship between the model of the planes and the plane themselves, the relationship between the model of the planes, and their number. From this, the researcher will be able to come up with a relatively accurate assumption of the amount of fire that they should expect, and as such, come up with the appropriate strategies to counter them. In effect, the quality of the strategist’s research will have been improved. This improvement will lead to enhanced impact, for the decisions made will be more practical and effective.

It is also very important for the researcher to differentiate between reflective and formative measures for the variables (Loomba 8). Appropriate statistical tools will then be formulated to avoid misspecifications (Loomba 8). It is common for most quantitative management studies to make use of reflective measures. This means that the covert, unobserved and latent variables are expressed in the measure that the researcher has adopted. For example, a variable like whether or not the customer is satisfied with the product is unobservable. The researcher will use reflective measures, such as asking the customer, “How do you like this product or service”.

Ex-Post Correction of Measurement Errors

Echambachi et al are of the view that a quantitative management researcher can correct reliability estimates of a single item measure by using a pre-determined scale (1810). The scale has known reliabilities in a “structural modeling framework” (Echambachi et al 1810).

Econometric researchers hold that if a measurement error is to occur when dealing with an independent variable, the result may be that it will be correlated to the error term in the estimation equation (Bertrand & Fransoo 260). Instrumental variables can be utilized to correct the bias.

Error in Determining the Relationship of Variables

A quantitative management research model is made up of various variables, and the researcher has to determine how these are interrelated. A problem affecting the quality and impact of this form of research is when the researcher is unable to make the correct connection and relationship between these variables (Faboxxi et al 37).


One form of relationship between the variables is causality. Which variable causes the other? For example, in market research that has the price of product and number of sales as variables, which between the two causes the other? It is advised that to improve the accuracy of determining the relationship, the researcher should first establish the relationship theoretically before embarking on the unearthing of empirical evidence to support the same. For variable A to cause B, three conditions must be met:

  1. the concomitant variation must exist between A and B
  2. clear temporal ordering of A and B must be proved and
  3. all other spurious influences that might affect A and B must be brought under control

The quantitative management researcher, for them to improve the quality and impact of their work, should strive to establish that one-way causality (in other words, that A causes B and not the other way round) exists. This they can do by availing robustness checks that must, in turn, meet three conditions:

  1. Eliminates reverse causality (Lee 220). B should not, under any circumstances, be proved to cause A, otherwise, the causality claim is nullified.
  2. Show the elimination of omitted variable bias (Bertrand & Fransoo 264).
  3. Make sure that the correlations involved are “robust to different specifications [and samples]).

At the end of the day, the quantitative researcher proves to the cynical of all readers that the proposed one-way causality (A causes B) is the only option left (since B does not cause A).


According to Echambachi et al (1811), this takes place when the independent variable of the model happens to be correlated with the error terms, known or unknown. If the researcher disregards Endogeneity, they may end up with biased estimates lacking inconsistency. Endogeneity can stem from various sources. They include reverse causality (B causes A in the above example), simultaneous causality (A causes B, and B causes A), and omitted variables (Loomba 2).

It is up to the researcher to check whether endogeneity is a risk in their research. This they can do by use of Hausman test (Echambachi et al 1819). Here, the researcher checks whether least squares and the estimates of the variable are different from each other in terms of statistics. Lack of endogeneity is proved when both coefficients are consistent with each other and have zero difference (Echambachi et al 1819). However, if the two are different, the researcher should realize that endogeneity exists. Reverse causality can be eliminated by the use of panel data or experiments aimed at isolating them (Lee 218). If the researcher feels that endogeneity is caused by an omitted variable, the researcher can right this by including an adequate measure for the particular variable that had been omitted (Lee 218).

Recording Interactions between Variables

Any quantitative management researcher aims to apply theories and hypotheses to real-life situations. When this application is carried out, it might emerge to the researcher that the relationship between two variables in the model is affected by interplay with a third variable. This interaction has to be captured for the researcher to come up with contingency provisions. For example, the researcher might find out that the effect that the price of a product has on sales is affected by the availability of a substitute. If the researcher realizes this, he will be able to come up with contingency plans to counteract the effect in a real-life situation.

The quality of the research and the impact that the result will have in real-life situations will be improved if the researcher can determine the extent to which variable C affects the interplay between variable A and B. for example, to what extent does the availability of a substitute affect the effect of price increase on the volume of sales? If this is not explicitly determined, the manager who will use the results of the research to make decisions will have a hard time coming up with strategic decisions in placing the particular product in the market.

Importance of Improving the Quality of Quantitative Management Research

The importance of ensuring that the results of quantitative research methods are as accurate as possible cannot b downplayed. For one, it ensures that the findings have a wider and effective impact on the consumers. The marketing manager will find it useful in making decisions concerning the placement of the product in the market, the military strategist will find it useful in coming up with strategies to counter the fire of the enemy.

As earlier indicated, the managers are operating in a very competitive environment today. Decisions need to be made daily, and there is no room for error. For example, if the marketing manager fixes the price of the product above that of the competitor, clients will be lost. On the other hand, if the price is fixed too low, the company may make losses. On the other hand, if the military strategist makes the wrong decisions, the war may be lost and negative repercussions accrued.

To improve the accuracy of the findings, it is important for the researcher to clearly define the research problem. For example, in military maneuvers, the research problem may be how to come with effective strategies to win the war. In marketing, the research problem may be the price to fix to increase sales and make profits. The researcher, having defined the problem, will embark on the process of collecting data and coming up with probable models. The third step involves articulating the problem with the models so developed. It is after this articulation has been carried out that the researcher can come up with the best military maneuver, and the best pricing strategy for the marketing manager.


Quantitative management is indispensable to the latter day’s manager. It is what they use to come up with decisions to address problems they face daily as they carry out their duties. This been the case, it becomes very important for the managers to improve the models that they apply in coming up with the decisions. This is the only way that they can come up with relevant and effective decisions. It is only through research that they can improve their models. It is in this light that it becomes pertinent to improve the quality and impact of quantitative management research.

To improve this quality and impact, it becomes important to identify the possible problems that may be encountered, which might affect the relevance of the results. Some of the problems that were identified in this research include the poor definition of variables, erroneous determination of the relationship between variables and their interaction. It is important to note that these are not the only problems that plague quantitative management research models. It is through addressing the problems that the quality and impact of the process can be improved.

Works Cited

Bertrand, Will M., & Fransoo, Jan C. “Operations Management Research Methodologies Using Quantitative Modelling,” International Journal of Operations and Production Management, 22(2), 2002. 241-264.

Echambachi, Raj, Campbell, Benjamin & Rajshree, Agarwal. “Encouraging Best Practice in Quantitative Management Research: An Incomplete List of Opportunities.” Journal of Management Studies, 43(8). 1801-1820. 2006. Web.

Faboxxi, Frank J., Focardi, Sergio M., & Jonas, Caroline. “Challenges in Quantitative Equity Management.” The Research Foundation of CFA Institute, 2008. 37-39.

Hahn, Martin. Quantitative Management Theory. 2007. Web.

Lee, Cheng-Few. “Overview of Quantitative Finance and Risk Management Research.” Rutgers University, 2008. 212-220.

Loomba, Arvinder. “Quantitative Research Methods.” International Journal of Operations and Quantitative Management, 24(9), 2009. 2-8.

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