Abstract:
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Eyewitness misidentification is the main cause of wrongful convictions, and is mainly due to false
memories and lacking police procedures. False memories can be said to have occurred when
people believe they have seen or experienced an event that did not occur or, at least, not in the
way they remembered. This is a common problem in witnessing crime. A group of researchers at
Lund University developed a software implemented algorithm capable to detecting false
memories. This algorithm is intended to be used as the core of a new product, a forensic
technology, which the police can use to minimize eyewitness misidentification.
The thesis first describes a generic theoretical framework to analyze a market opportunity for new
product concepts. When applying this framework to this particular forensic technology, PEST
analysis shows low influence of external factors, although uncertainty is fairly high. Porter's five
force analysis suggests that the industry is less attractive due to high buyer's power, a large
number of competitors and high rivalry. Finally, SWOT analysis shows an equilibrated number of
positive and negative elements in both internal and external features. Therefore, in order to
successfully enter the identified market, some issues must be addressed, such as the need of a
viable prototyping process and an understanding of the ethical and public aspects of the
technology. Two different strategies are proposed for market entry. The first one is a lead-user
approach that seeks early consumer involvement of the, and the second is a cooperation/licensing
strategy with integration of the technology into a pre-existing industry solution |