26th International Association for Management of Technology Conference - IAMOT 2017

Monitoring of emerging technologies needs indicators which are more sensitive to changes in context and time (Porter, 1991). To this purpose, organizations have depended on qualitative expert judgment, but it has increasingly suffered from reduced sensitivity and accuracy (Ranjbar and Tavakoli, 2015). A better way of monitoring is needed. As either alternative or supplement to expert judgment, some researchers suggested various quantitative indicators including number of patents, number of new products and others (Valk et al., 2009). However, it is not clear what indicators are good for monitoring of emerging technologies, and how we can identify such indicators because each indicator has advantages as well as disadvantages. Therefore, in this paper, we suggest a method of identifying appropriate quantitative indicators for emerging technology monitoring. In the existing literature, appropriateness of monitoring indicators can be evaluated from three perspectives: 1) sensitivity to technological changes, 2) sensitivity to changes in external context, and 3) robustness to noises. In other words, good monitoring indicators send signals when important changes occur, but move little according to noises and accidental events. Through literature review, we collect monitoring indicators and their time-series data of four emerging technologies over last five years. Selected technologies consist of building-integrated photovoltaics system, lumber support, 3D animation platform, Probiotics and automatic insulin pump. We define changes in technology, context and noise through document analysis of papers, patents and news, and measure sensitivity of each monitoring indicators to three factors by using the first-order sensitivity index. The index measures the contribution of each change alone to the variance in monitoring indicators. For five emerging technologies, we find some indicators that are sensitive to more than 70% of changes in technology and external context, but are sensitive to less than 10% of noises. These are good general indicators. However, some indicators are sensitive to more than 90% of changes in either technology or external context, and therefore are useful to monitor a certain kind of changes. By using these results, we can create a sequential use of indicators to identify a specific change in either technology or external context, and then to identify a clear shift in both aspects. Our finding can be useful to monitor emerging technologies on both public and private sides, and help policymakers as well as managers make good decisions for science and technology policy, R&D and further business.