How to predict the development of breakthrough technologies with the help of electronic databases?

Breakthrough technologies can be defined by ‘new-to-the-world’ or ‘radical (improved)’ technologies which have the capacity to change the behaviour of end-users. The journey these technologies practise towards the mainstream market can be regarded as a dynamic process with lots of uncertainties. Companies investing in the development of these technologies face some serious risks. For the managers of these companies it would be of tremendous value if they could, even in the slightest way, make strategic decisions supported by reliable forecasts. This research aims to investigate the added value of electronic databases in determining the chances of succeeding in the market. Different kinds of electronic databases can measure the activity on a specific topic, which subsequently can be used in forecasting whether the activity will increase or not. This information, in combination with current forecasting methods, can be applied in a business intelligence tool; a tool supporting the decision making process of managers. One of these databases, besides the scientific and patent databases, is offered by Google News and includes business press and news articles from many different sources. This database indicates the activity and popularity on a particular topic among future consumers. Because of its potential, this database is included in this research as well. To answer this challenging question about the added value of electronic databases, two analyses were performed using data from 14 breakthrough technologies in the material- and pharmaceutical industry. The first analysis included different viewpoints in literature on scientific-, technological-, and market activity and when the databases appear to show the highest activity over the life-cycle of a technology. Then, the analysis based on these 14 cases, is used as verification. As a result, it became clear that scientific and market activity increases over time in parallel. The second analysis focused on a completely different aspect. A further dive was made into the history of these technologies, looking for a correlation between the patterns generated by databases and the historical patterns. Remarkably, about 50% of the cases showed a correlation with the patterns generated by Google News. Although this result seems initially not significant, future research is proposed, where even higher results might be found. Then, this database might be of added value for future forecasting tools. This explorative study adds new and improved perspectives on scientific and managerial aspects. It contributes to the concept of forecasting the development of breakthrough technologies. Also, it clearly shows the added value of electronic databases and what they could mean for future research. Nevertheless, this study bears with some limitations. The small sample size, the focus on only two industries, noise in the data, and the lack of more effective queries during the search ensure an inevitably bias in the results. However, the explorative nature of this study does supply the first large building block on this topic, which will be used in future research.

Subject

prediction of technological diffusion
technology life-cycle
large-scale production and diffusion
breakthrough technologies
forecasting methods
business intelligence

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