The chapter concerns the measurement and forecasting of technological change, a topic relevant to many high-tech organizations and their customers. We revisit recent and classic data sets from technology forecasting data envelopment analysis (TFDEA) research and technometrics in light of a new visualization technique known as t-Distributed Stochastic Neighbor Embedding (t-SNE). The technique is a nonlinear visualization technique for preserving local structure in high-dimensional spaces of data. The technique may be classified as a form of topological data analysis. Specifically, each point in the space represents a potential technological design or implementation, and each line segment in the space represents a local measure of technological improvement or degradation. We hypothesize six distinct kinds of performance development in technology within this space, including the frontier, the fold, the salient, the soliton, and the lock-in. Then we examine the spaces to determine which kinds of development are the best explanations for observed development. The technique is not extrapolative and therefore cannot fully supplant the existing technometric methods. Nonetheless the approach offers a useful diagnostic to the existing technometric methods and may help advance theories of technological development.

User Generated Content (UGC) requires new business intelligence methods to understand the influence of online opinion formation on customer purchasing decisions. We developed a conceptual model for deriving business intelligence from tweets, based on the Classical Model of Consensus Formation and the Theory of Planned Behaviour. We applied the model to the dynamic high-tech smartphone market by means of three case studies on the launch of new smartphones. By using Poisson regression, data-and sentiment-analysis on tweets we show how opinion leadership and real-life events effect the volume of online chatter and sentiments about the launch of new smartphones. Application of the model reveals businesses parameters that can be influenced to enhance competitiveness in dynamic high tech markets. Our conceptual model is suitable to be turned into a predictive model that takes the richness of tweets in online opinion formation into account.