連續併購（Serial M&A）是重要的企業成長策略，但其對創新的累積影響尚待探討。本研究基於知識基礎觀點與組織學習理論，將連續併購定義為一種利用型學習過程，旨在檢視其對企業創新成果的影響，並探究吸收能力（研發投入）與組織規模的調節作用。
本研究以 2009–2019 年 472 家美國上市高科技公司為樣本，採用負二項與分數羅吉斯迴歸模型進行實證分析。研究發現，連續併購顯著提升了企業的專利產出，並增加了技術重疊的「利用型創新」比例。此外，強大的研發基礎與較大的組織規模會產生縱效，共同放大這些創新效益。
研究結論指出，在高吸收能力與靈活組織結構的支持下，連續收購能系統性地增強既有技術軌跡上的創新表現。本研究透過引入專利重疊指標捕捉知識重新配置，並證實吸收能力與組織慣性是收購驅動創新的關鍵邊界條件，對併購與創新領域文獻具有理論貢獻。

Serial mergers and acquisitions (M&A) are a common growth strategy, yet their cumulative impact on innovation remains underexplored. Drawing on the knowledge-based view and organizational learning theory, we conceptualize serial acquisitions as an exploitative learning process that enables firms to redeploy knowledge for incremental innovation. We further posit that absorptive capacity (R&D intensity) and organizational size moderate these effects. Analyzing 472 U.S. high-tech firms (2009–2019) using negative binomial and fractional logit models, we find that serial M&A significantly increases patent output and the share of exploitative innovations. Specifically, both a robust R&D base and a larger organizational size synergistically amplify these innovation benefits. Our study contributes to M&A and innovation literature by introducing a refined patent-overlap metric and identifying absorptive capacity and structural inertia as critical boundary conditions for acquisition-driven innovation.

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