Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S Curves

This study aims to propose a technology forecasting approach based on hierarchical S-curves. The proposed approach uses holistic forecasting by evaluating the S-curves of sub-technologies as well as the main technology under concern. A case study of unmanned aerial vehicle (UAV) technologies is conducted to demonstrate how the proposed approach works in practice. This is the first study that applies hierarchical S-curves to technology forecasting of unmanned aerial vehicle technologies in the literature. The future trend of the UAV technologies is analysed in detail through a hierarchical S-curve approach. Hierarchical S-curves are also utilised to investigate the sub-technologies of the UAV. In addition, the technology development life cycle of technology is assessed by using the three indexes namely, (1) the current technological maturity ratio (TMR), (2) estimating the number of potential patents that could be granted in the future (PPA), and (3) forecasting the expected remaining life (ERL). The results of this study indicate that the UAV technologies and their sub-technologies are at the growth stage in the technology life cycle, and most of the developments in UAV technology will have been completed by 2048. Hence, these technologies can be considered emerging technologies.

Forecasting Technology Trends through the Gap Between Science and Technology: The Case of Software as an E-Commerce Service

Identifying technology trends can be a key success factor for companies to be competitive and take advantage of technological trends before they occur. The companies always work to plan for future products and services. For that, it is important to turn to methods that are used for technology forecasting. These tools help the companies to define potential markets for innovative new products and services. This paper uses text mining techniques along with expert judgment to detect and analyze the near-term technology evolution trends in a Software as a Service (SaaS) case study. The longer-term technology development trend in this case is forecasted by analyzing the gaps between science and technology. This paper contributes to the technology forecasting methodology and will be of interest to those in SaaS technology. Our findings reveal five trends in the technology: 1) virtual networking, 2) the hybrid cloud, 3) modeling methodologies, 4) mobile applications, and 5) web applications. Among the results achieved, we can summarize the interesting ones as follows: it is possible to say that traditional information systems are now evolving into online information systems. On the other hand, the use of a licensing model based on subscriptions triggers the change in perpetual licensing models. The product range that has evolved towards mobile technologies has put pressure on information storage technologies and has led to the search for new methods especially in the development of database systems.