The following are the hypothesis for the proposed study:
H0: The implementation of the GDPR has helped to create a business environment where the risk associated with sharing personal information is eliminated;
H1: The implementation of the GDPR has done nothing to create a business environment where the risk associated with sharing personal information is eliminated.
Contribution to Knowledge
The GDPR took effect in May 2018 after it had been passed on 14 April 2016. In essence, the new regulations helped to ensure the free movement of personal information between the member states of the European Union. The regulations were supposed to offer citizens of the European Union more control over how their personal information was shared across the EU borders. Therefore, the proposed study will help to contribute to the available knowledge regarding the effectiveness of the GDPR Regulations. Besides, this should help future researchers intending to study the subject, consider it in more detail.
Information evaluating the effectiveness of the GDPR in mitigating risks associated with sharing personal data can be found in different sources from academic journals. Therefore, the research method adopted for the proposed study will use a thematic analysis. Thematic analysis was chosen after an examination of the advantages of the approach.
The decision to adopt the thematic analysis came from considering the advantages and disadvantages of the method. An evaluation of the advantages of the method shows that one can adopt an inductive or theoretical approach when exploring a given subject. When an inductive thematic analysis is adopted, the data collected is strongly associated with the phenomenon under investigation. On the other hand, a theoretical thematic analysis is conducted based on theories seen in the work completed. It is this flexibility, and the allowance offered to select one strategy over another depending on suitability that made it more suited for the study. Similarly, the approach allows for pragmatisms and could be effective when collecting and analyzing large chunks of data.