Identifying Transformative Technologies on Emerging Stage: Monitoring Dynamic Evolution of Weak Signals

Portland International Conference on Management of Engineering and Technology (PICMET), pp. 1–9

Abstract

Transformative technologies have the potential to significantly alter existing technological paths and business models, accelerating the industrial change process. Identifying and monitoring transformative technologies early is critical for both corporate investment decisions and government research & development (R & D) strategy. Current studies on identifying and monitoring transformative technologies have primarily focused on technologies in the ascent phase. Few research performed on those in the emerging stage. Given gaps in current research, this paper proposes an early identifying framework for transformative technologies based on their dynamic evolution process. First, we discover weak signals of transformative technology using Subject-Action-Objective (SAO) semantic mining and an outlier detection algorithm based on discontinuity features. After identifying weak signals, our research methodology involves monitoring the developing trends from two different perspectives. Firstly, we scrutinize whether research on weak signals has progressed from marginal to core research over time using an outlier detection algorithm. Secondly, we investigate the convergence of weak signals with respect to both the overall technology landscape and research content within technology levels. This is achieved through the utilization of the Latent Dirichlet Allocation (LDA) and Dynamic topic models (DTM). We take research into All-Solid State Battery (ASSBs) as a case study. The results present the ASSLBs exhibit characteristics from edge to core study. Also, it develops two primary technology tracks: All-solid-state lithium-ion batteries (ASSLIBs) and All-solid-state lithium Metal batteries. These two domain technologies display a development pattern from divergence to convergence research. The case verifies the feasibility and validity of the proposed framework. Our work contributes to provide new insights into identifying disruptive technologies in the emerging stage.

Authors 3

1. Qianqian Xie Aachen Centre for Science and Technology Studies

   Leiden University

   Affiliation as printed

   Leiden University,Center for Science and Technology Studies (CWTS),Leiden,Netherlands

   Center for Science and Technology Studies (CWTS), Leiden University, Leiden, Netherlands

2. Wenya Ma

   Beijing University of Technology

   Affiliation as printed

   Beijing University of Technology,College of Economics and Management,Beijing,China

   College of Economics and Management, Beijing University of Technology, Beijing, China

3. Xin Li

   Beijing University of Technology

   Affiliation as printed

   Beijing University of Technology,College of Economics and Management,Beijing,China

   College of Economics and Management, Beijing University of Technology, Beijing, China