Business Ethics: Ethical Decision Making & Cases, Research Paper Example

In the process of conducting this survey questionnaire experiment, we will need to establish and gather a sample population to use as the subjects for the study. There are various ways to approach how we would establish a population sample. The most effective way to gather a sample population from the organization would be to randomize it. This means collecting a simple random sample set. However, we would need to take all precautions in the process of selecting random samples. For example, it would be unfair if the majority of the sample population were mostly men or mostly woman. This would open up the doors for biased assumptions from the survey analysts based on possible gender discrimination. Any and all types of biased assumptions would need to be fully eliminated from this questionnaire study as we want it to remain as fair and as ethical as possible. The issue of ethics is already in dilemma which is the reason for the study in the first place. It would be unnecessary to create more ethical issues in the process of this study of the organization (Zikmund, Babin, Carr, & Griffin, 2013).

The questions we hope to have answered at the conclusion of this study are questions such as: Does one gender tends to understand the code of ethics better than the other? How well is the code of ethics understood by the organization in general? How well has management done in regard to enforcing the code of ethics? The variables surrounding our hypothesis include the independent variable of gender and the dependent variable of how well employees understand the code of ethics. These questions align with and support the hypothesis because the hypothesis stands that continued failure to abide by the code of ethics would compromise business operations with unnecessary drawbacks in the work environment. This hypothesis all is based on the level of understanding of the code of ethics (Zikmund, 2013).

The best way to maximize the effectiveness of the random sample selection is to keep the selections as random as possible. However, at the exact same time we would like to maintain an even distribution between genders. We can do this by clustering two sets of random sample populations from the organization. Of the 80 employees that work for the organization, we will collect a total sample population of 50 subjects. What this means is that we can still randomize the selections but they would be separated by gender. Simply, we would randomly select 25 men for the first cluster and then 25 women for the next cluster. We are choosing 50 for our population because it is a convenient sample size of the 80 employees in the organization. 50 out of 80 also gives the perception of a “majority rules” approach. Using this approach, we would ensure the survey analysts that there is an even distribution between men and woman. We can also assure them that all sample selections were randomized and no single employee was hand-picked for purposes of political influence (Stanwick & Stanwick 2013).

We will get to the population through official electronic messages containing the questionnaire. To ensure that the collection of the population remains random, we have borrowed software from a third party IT department to send out the e-mails containing the questionnaire. This software allows for all employees’ names and official work e-mails to be input into a pool. The software can then select 50 random samples from the pool and send them the questionnaire all at once. The samples will be informed of their confidentiality being maintained and be instructed to submit the questionnaire upon completion. Once all questionnaire have been submitted, analysis can begin(Hair, Money, Samouel, & Page, 2007).

With this very useful approach, we have already eliminated possible biases. We have eliminated the possibility of gender bias by evenly distributing both genders in the organization. We have also eliminated the possibility of political biases by keeping the sample selection randomized and out of any one employee’s favor; which eliminated the conflict of favoritism in this study (Hair, 2007).

This method is also convenient for the survey analysts as it requires less work for them on the selection process and allows them more time to focus on the analysis of the survey results itself. Because the selection process is so randomized, there will be no need for any long interview processes in subject selection; which would create more bias approaches anyways (Ferrell & Fraedrich, 2014).

Another possible bias is the conflict of interest. If a participant of the sample population is also a member of the research analyst team, then this would create a conflict of interest for that researcher to skew the results of the study in their opinionated favor. This would not only draw false or inaccurate conclusions about the study, but it would also be unethical. In order to eliminate this bias, it must be assured that no member of the research analyst team is a participant in the study. To provide an even stronger safe guard, external entities from outside of the organization should be include on the research analyst team to audit the findings. This way, no member of the organization can skew the results in favor of their peers in the organization (Zikmund, 2013).

References

Ferrell, O. C., & Fraedrich, J. (2014). Business ethics: Ethical decision making & cases. Cengage learning.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education+ Training, 49(4), 336-337.

Stanwick, P., & Stanwick, S. D. (2013). Understanding business ethics. Sage.

Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods (9th ed.). Mason, OH: South-Western.