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Business Research Ethics

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Business Research Ethics

According to Business Research Methods (Cooper & Schindler, 2011), ethics are behavior standards that guide moral choices. The goal of ethics in research is to ensure no one is harmed or suffers consequences because of the activities or findings of the particular research findings. Although moral ethics exist, there are often times that unethical research practices take place and result in deception, violation of non-disclosure agreements, invoicing irregularities and breaking participant confidentiality just to name a few (Cooper & Schindler, 2011). The purpose of this paper is to explain the unethical behavior displayed by Netflix, which resulted in the filing of a 2009 class action lawsuit. The paper will identify the injured parties, explain how the unethical behavior affected the organization and explain how the unethical behavior can be avoided in the future and how it was resolved in this case.

In October 2006, Netflix launched a contest that was open to the public offering the winner one million dollars to come up with a collaborative filtering algorithm that could predict its subscriber's future movie watching better than the system it was currently using (Jane Doe,Nelly Valdez Marquez, Anthony Sinopoli and Paul Navarro vs. Netflix Inc, 2009). To process their analysis, each contestant or team of contestants were provided with two different data sets. The first was a "training data set" that contained 100 million subscribers' movie ratings. The ratings included a numeric identifier unique to the subscriber, movie title, year of release, date of subscriber rating, and the rate of one to five stars assigned by the subscriber (Jane Doe,Nelly Valdez Marquez, Anthony Sinopoli and Paul Navarro vs. Netflix Inc, 2009). The second data set was a "qualifying data set". This data set contained 2.8 million ratings in the same format as the training data set only it omitted the rating assigned by the subscriber. This information was known to the judges to analyze whose information was most accurate.

The data sets were supposed to be scrubbed of all personal information such as name, phone numbers, addresses etc. and the remaining information was supposed to be provided to the contestants as anonymous data. However, Netflix failed to sufficiently de-anonymize its user data and its anonymization process was able to be reversed by researched. This provided all private information about each of the subscribers that were included in the data sets. Netflix knew before the contest began that the data mining process to anonymize the information could be compromised because of the same situation happening to AOL just two months before however it continued with the contest. As a result, individuals and entities were able to obtain Netflix subscriber's data for commercial, personal and other uses without the consent of the subscriber (Jane



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