Future 450mm semiconductor wafer foundries are expected to produce billions of low cost, leading-edge processors, memories, and wireless sensors for Internet of Everything applications in smart cities, smart grids, and smart infrastructures. The problem has been a lack of wise investment decision making using traditional semiconductor industry models. The purpose of this study was to design decision-making models to conserve financial resources from conception to commercialization using real options to optimize production capacity, to defer an investment, and to abandon the project. The study consisted of 4 research questions that compared net present value from real option closed-form equations and binomial lattice models using the Black-Scholes option pricing theory. Three had focused on sensitivity parameters. Moore's second law was applied to find the total foundry cost. Data were collected using snowball sampling and face-to-face surveys. Original survey data from 46 Americans in the U.S.A. were compared to 46 Europeans in Germany. Data were analyzed with a paired-difference test and the Box-Behnken design was employed to create prediction models to support each hypothesis. Data from the real option models and survey findings indicate American 450mm foundries will likely capture greater value and will choose the differentiation strategy to produce premium chips, whereas higher capacity, cost leadership European foundries will produce commodity chips. Positive social change and global quality of life improvements are expected to occur by 2020 when semiconductors will be needed for the $14 trillion Internet of Everything market to create safe self-driving vehicles, autonomous robots, smart homes, novel medical electronics, wearable computers with streaming augmented reality information, and digital wallets for cashless societies.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-1092 |
| Date | 01 January 2014 |
| Creators | Pastore, Thomas Earl |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Walden Dissertations and Doctoral Studies |
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