Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles – one particularly promising contender within the field of nanoenabled drug delivery. Several text mining techniques – term clumping, SAO technique, and net effect analysis – as well as technology roadmapping, are combined with expert judgment to identify the main areas of R&D in this field, and to track their evolution over time. Through analysis, data from multiple sources, including research publications, patents, and commercial press, reveal possible future applications and commercialization opportunities for this emerging technology. We find that research is moving away from materials and delivery outcomes toward clinical applications. The most promising markets are pharmaceuticals and cosmetics; however, the “time-to-market” is much shorter for cosmetics than it is for pharmaceuticals. The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.

Introduction

New and emerging science and technology has attracted great attention among scholars because of its tremendous potential to improve society and stimulate economic development. However, the threats and opportunities inherent in such technologies can cause developers to proceed with caution. On the one hand, the existing competitive advantages inherent in current technological competencies offer stability, and new technology may threaten these advantages or even eliminate entire markets. On the other hand, early analysis of new technical areas may present opportunities to take the lead before other competitors become entrenched (Guo et al., 2015). Therefore, developing ways to assess the current research focus and future development directions of new and emerging science and technologies is a compelling issue. Technology opportunity analysis (Porter et al., 1994), which applies data mining and text mining tools to ST&I resources to detect technological innovation (Ma et al., 2014 and Ma et al., 2016) offers one possible solution to this problem. The process allows analysts to explore opportunities for transforming new technologies into new products and, thus, provides decision support to researchers, R&D planners and managers, and science policy-makers (Lee et al., 2015). A number of technology opportunity studies that rely on text mining technologies have been conducted to help derive information for competitive technical intelligence analysis, technology development trend analysis (Ma and Porter, 2015), and forecasting (Ailem et al., 2016; Song et al., 2017).