Archive Number: 4.731
Status of Thesis: In Progress
Date of start: 2021-01-01
Background:Beginning in the mid-2000s the Direct-to-Consumer (DTC) genetic testing market started expand-ing rapidly, due to plummeting costs in human genome sequencing. Rising interest of the public and a do-it-yourself mentality in American culture have led to a plethora of global players adapting diverse business models.
However, many medical professionals argue, that DTC genetic health tests are performed with insufficient accuracy and information provided may be misleading. While the risk of misinterpreta-tion may not be as critical for nonmedical genetic testing, such as ancestry and lifestyle tests, some experts and consumers further criticize DTC genetic testing service providers’ transparency on genome privacy and ethicality. Additionally, the DTC genetic testing market is still largely un-regulated, allowing service providers the arbitrary use of disputed business models and genetic information therein.
For example, 23andMe, one of the largest service providers, is known to undertake the business practice of reselling their customers’ genetic data to clinical research and biopharmaceutical com-panies to increase revenue. However, consumers are often not aware of these business practices and that they agree to these disputed terms upon purchasing the service. As a result, when cus-tomers learn about the hidden business practices, they are sometimes dissatisfied with the service they received. This lack of transparency and ethicality may cause consumers to perceive certain DTC genetic testing business models to be unfair.
Therefore, unveiling consumers’ fairness perception can be valuable information for retailers and marketers, creating awareness of issues of (un)fairness in business models and its influence on consumer behavior. Moreover, fairness has the potential of addressing many of the concerns crit-ics have towards DTC genetic testing.
Objective(s): The aim of this assignment is to analyze consumers’ fairness perception of DTC genetic testing business models. For this hierarchical Bayes choice models need to be tested and compared in terms of accuracy. Analysis may be carried out with various software solutions (SAS, JMP, R or Python Frameworks, etc.). Further, different consumer groups are identified by means of segmentation analysis.
Method(s): Discrete Choice Experiment (Analysis with hierarchical Bayes choice model), consumer data will be provided
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