To what extent does electronic database information reflect the process of innovation?

This research aims to investigate whether adding electronic database information with the technology life cycle reflects the process of innovation. The chances of successfully market a news product are dramatically low, resulting in the need for methods to forecast the process of innovation. A well-known method is the use of market research. However, for specific cases, there is reason to doubt the validity of market research, since firms are not able to meet all the required conditions. (Ortt et al., 2007) This research aims to investigate whether the use of electronic databases can be used to reduce the risk in the process of innovation. An analysis between the various exiting Technology Life Cycle (TLC) models, the TLC model of (Ortt and Schoormans, 2004), appeared to be in the product category unit of analysis, while other innovation theories analyze the process on product or technology level. This model would constitute to best model to use for forecasting, seen the scope, the valid y-axis presentation (industry rate of adoption) and the use of hallmarks. For the theoretical scientific activity over the TLC, many scholars assume a scientific and patent double boom cycle. (e.g. (Schmoch, 2007)) Remarkable little attempt have been found regarding the theoretical news distribution over a technology life cycle. When defining a methodology in order to find consistencies, it is recognized that every quantification of a process with humans involved displays an erratic pattern. This vision results from the fact that the current stage of evolution cannot understand (or to a limited extent) the decision making model of a human brain. The methodologies to analyze the data quantitatively are formulated taking the expected erratic pattern in mind. The technology life cycle combined with the news, scientific and patent distribution is, in this thesis, defined as the innovation diffusion graph. Two types of analysis were performed for this research: qualitative and quantitative. The qualitative propositions are derived on the basis of the innovation diffusion graphs, while the quantitative propositions are derived on the basis of the average level, dispersion and slope. Defining the statements on the basis of every graph and data prevents neglecting of important results. It was found that scientific activity diffuses faster than patents (78%), were the uptake in patents was later than the uptake in scientific research (64%). In 71% of the cases, scientific research diffused before the hallmark large scale diffusion. A counter intuitive result came from the uptake in patent with respect to sales, were in 57,1% of the cases the patent uptake was later than sales (28,6% equal uptake; 7,1% patent before sales and 7,1% undetermined). For the pharmaceutical industry the news followed a parabolic trend, with its highest point in the market adaptation phase. The average level of news (material), scientific (both industries) and patent (both industries) all followed a linear upward trend in the majority of the cases. No evidence of the in the literature assumed double boom cycle for the patents and scientific activity is found. Furthermore, (Tushman and Anderson, 1986) found a significant increased uncertainty after a technological discontinuity. No evidence of an increased uncertainty was found.Technology, Strategy and EntrepeneurshipTechnology, Policy and Managemen