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| Research on Weak Signal Detection Models for Key Derivative Technologies along the Innovation Chain of Critical Core Technologies |
| Ye Guanghui1, Tu Kai1, Han Li1, Hu Lina1, Xiong Bingqiao2 |
1.School of Information Management, Central China Normal University, Wuhan 430079
2.Strategic Studies Institute of Yangtze Laboratory, Wuhan 430010 |
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Abstract Continuously tracking the key derivative innovation technologies along the innovation chain of critical core technologies requires technological foresight. Only in this way can enterprises avoid having their research and development (R&D) agendas and scientific and technological (S&T) resource-allocation decisions of relevant agencies sink into a fog of technological information. Drawing on Hiltunen’s triadic model of the future sign and integrating complementary lenses, such as fitness landscape and information foraging theories, we constructed a multi-theoretically grounded weak-signal detection model for key derivative innovation technologies. Within this model, weak signal detection is operationalized as a three-stage process: signal visibility measurement, signal diffusion measurement, and signal sense-making. To address issues such as the isolated interpretation of weak signals and signal disappearance, a weak-signal detection model was applied at the thematic level to validate the model’s effectiveness and explore the evolutionary patterns of specific instance technologies. The critical core technology of Transformer in the field of artificial intelligence, was selected for empirical analysis. By acquiring and fusing heterogeneous data, the weak signals of key derivative innovation technologies were unearthed at different stages and their subsequent evolutionary trajectories were forecasted. From the second half of 2018 to the first half of 2020, Transformer developments clustered in natural-language processing, and several spin-offs, such as GPT-style models, were detected as weak signals. Between the second half of 2020 and the first half of 2022, Transformer advances shifted to computer vision, with weak signals detected for spin-offs, such as image classification. From the second half of 2022 to the first half of 2024, weak signals emerged in multimodal applications, indicating substantial future potential. The weak signal detection model advanced in this study not only enriches the theoretical landscape of technology foresight but also furnishes firms and agencies with a strategic compass for situating their R&D portfolios.
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Received: 03 January 2025
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