Technology opportunity analysis (TOA) traditionally examines technological developments to identify opportunities, but typically conceptualizes these opportunities at the level of technologies or fields. This study advances practice-level TOA by conceptualizing technology-enabled innovative practices as technology-process-industry configurations and using their evolution to identify technology opportunities. It extends TOA by integrating diffusion of innovation theory and operationalizing its constructs to explain how opportunity spaces evolve. A seven-stage evolutionary model tracks how configurations emerge, diffuse and consolidate over time, identifying an opportunity window where configurations remain vacant, exploratory, emerging or promising. To investigate this perspective and respond to recent calls for generative AI in informetrics, the study develops an AI-enabled Tech Mining framework based on large-scale analysis of full-text scientific articles. ChatGPT-4o operates within an LLM+ architecture, combining prompt engineering with a rule-based expert system. The framework is benchmarked against expert coders, near-graduate students, a fine-tuned BERT classifier and alternative LLMs, and applied to 129,566 articles (2019–2024). The resulting dataset of 43,632 practices across 11 enabling technologies is analyzed using supervised machine learning to identify plausible vacant configurations. The study shows how controlled LLM workflows can make theory-informed opportunity analysis scalable and support a more managerial understanding of technology opportunities.
This study introduces a framework that uses news-based organizational events as signals to diagnose socio-technical transitions. I examine a unique longitudinal dataset of daily corporate developments in the global FinTech industry from 1999 to 2025. The findings show that patterns in strategic actions map to four distinct transition phases: emergence, acceleration, industrialization, and stabilization. Each phase exhibits a distinct financial signature. The acceleration phase is particularly notable for combining the highest returns with the highest systematic risk, consistent with a high-risk, high-return profile. The analysis also reveals non-linear phase effects with optimal concentration ranges. For returns, the relationship with phase concentration is inverted U-shaped, peaking at 23.94% for emergence. For geographic diversification, the pattern is U-shaped, indicating that deeper transition engagement ultimately accelerates global expansion. Exogenous crises are associated with accelerated transition dynamics: showing increased emergence and industrialization while acceleration declines. By linking firm-level actions to system-level financial outcomes, the framework bridges the micro-macro divide in transition theory. It offers managers and policymakers a real-time diagnostic lens for navigating technological change. The analysis establishes a strong empirical foundation for future predictive models, though the current results identify only contemporaneous associations, not predictive causality.
While existing literature emphasizes the theoretical performance benefits of artificial intelligence (AI) in elite sports, the lived experiences, socio-technical negotiations, and future societal implications of this integration remain underexplored. Addressing this gap, this study employs an abductive, qualitative methodology utilizing the Gioia approach to analyze AI integration within elite sports ecosystems. Relying on the concept of information power, this research draws upon in-depth interviews with ten world-class informants, including Olympic and Paralympic champions as well as highly specialized medical staff. The findings reveal four core theoretical dimensions: (1) the algorithmic mediation of the biological body, (2) human-AI strategic symbiosis, (3) the data-driven reconfiguration of organizational trust, and (4) systemic implementation barriers. By conceptualizing these dimensions, the proposed model shifts the academic focus from isolated performance metrics to the holistic restructuring of high-stakes organizations. This paper forecasts the future trajectory of the algorithmic athlete, critically examining the risks of data surveillance, human intuition erosion, and the potential emergence of a technological divide. Empirically, this research provides actionable foresight for policymakers and sports managers to design robust ethical frameworks, ensuring equitable, transparent, and sustainable human-AI collaboration in future organizational models.
