Graduate and Professional Student Degree Program Research (Non-ETD)

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Browsing Graduate and Professional Student Degree Program Research (Non-ETD) by Author "Boles, Jillian R."

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    Estimating the Economic Costs of Espionage
    (2010) Bell, Rich; Bennett, J. Ethan; Boles, Jillian R.; Goodoien, David M.; Irving, Jeff W.; Kuhlman, Phillip B.; White, Amanda K.; Engel, Jeffrey A.
    Economic espionage is a serious threat to the vitality of the U.S. economy. While this is a widely accepted fact, there is no formal way to measure the damage an incident of economic espionage has on the U.S. economy. The U.S. government would like to know how damaging economic espionage is on the economy. However, the full repercussions of an incident of economic espionage are never known. A stolen trade secret, over the course of many years, could be used in different products and in different industries. The loss of a trade secret is an immeasurable value. Instead of attempting to measure such an overarching elusive concept, the research team sought to measure the potential consequence of economic espionage. In this study, the research team constructed a model to identify the severity of an incident of economic espionage and its consequences on the U.S. economy. The model was designed for use by federal government employees with the intent that the federal government could apply publically available case information to the model. The model provides a qualitative estimate of “consequence” as it relates to economic loss. The model generates a severity score between 0 and 1, which corresponds to a „low‟, „moderate‟, and „high‟ consequence. The severity score incorporates the model‟s four main variables into two primary components: „Industry‟ and „Case Variables‟. „Industry‟ assesses the significance of where the incident of economic espionage occurred. „Industry‟ is derived from a combination of the percentage of GDP in terms of value added for each of the 14 industries and the „susceptibility‟ of each of the 14 industries. This process enables the model to be individualized to a specific industry, which allows a different potential consequence to the U.S. economy. „Case Variables‟ assess the significance of the incident of economic espionage. „Case Variables‟ include the „Characteristics of the Theft‟, „Cost‟, and „Beneficiary‟ variables. The model requires the user to first select the „Industry‟ where the incident occurred and then to identify the „Case Variables‟. Therefore, the potential consequence on the U.S. economy from an incident of economic espionage is dependent on the industry. To greater individualize the model, the research team designed a method whereby questions within the model would matter more when compared to others. As no two incidents of economic espionage are identical, the research team developed a system of weighing the variables and their respective questions. With all the variables measured, standardized, and weighed against each other, the model calculates an overall severity score, which corresponds to the level of consequence for an incident of economic espionage.