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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.
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Mushtaq Hussain Khan (2026) studied this question.
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