Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations

Technological opportunities (TOs) are the potential and set of possibilities for technology advances in a given industry. When enterprises are able to catch and adapt to them in a timely manner, they can grab market share from competitors who have failed to adapt to these challenges. However, when there exist large gaps between enterprises and their competitors, it should be carefully evaluated whether enterprise-specific TOs are worth exploring. Moreover, faced with diversified competitive relations, enterprises also need to formulate differentiated research and development (R&D) strategies for different TOs. To address these research gaps, this paper argues for the theoretical concepts of technical windows (TWs), emerging technologies (ETs), and TOs, and proposes a three-stage framework to detect enterprise-specific TOs. This framework is empirically validated in 282,778 patents from the artificial intelligence (AI) industry of China. To begin with, this paper extracts 53 candidate technical knowledge in the AI industry based on topic modeling, which covers various application scenarios such as power, logistics and agriculture. Then, this work identifies 15 industry-specific TWs based on the impact, growth and novelty of technologies, which are calculated with pre-trained language model and aggregated mapping. Moreover, technological competitiveness that combines quality and scale is applied to carve out competitive positions of enterprises across different TWs, which is also utilized to screen out suitable TOs and formulate differentiated R&D strategies. The case of Baidu reveals these results in detail. At last, comparative and extensible fine-grained analyses confirm the robustness and theoretical value of this thesis in the technology innovation management.