本研究探討企業內部知識基礎與其在知識流網絡中的位置，如何影響其在探索型與利用型創新之間的轉換。在人工智慧產業中，企業如何於深化既有能力與開拓新技術之間取得平衡，已成為長期競爭力的關鍵。惟過去研究多採靜態觀點，鮮少同時考量網絡演化與行為轉換的內生過程。為彌補此缺口，本研究整合動態網絡觀點、知識基礎觀點與探索與利用型創新理論，建構企業層級之知識引用網絡，並提出三層次分析架構（一）企業間知識流動隨時間之演化（二）企業內部知識能力，包含知識組合能力、知識存量多樣性與技術距離，對探索與利用行為的影響（三）企業在引用網絡中的外部位置，包含企業在網絡中的重要性以及網絡效率，如何形塑其探索與利用行為。在研究方法上，本研究以人工智慧產業之專利資料作為實證來源，以國際專利分類主群組（IPC main group）作為知識元素之代表單位，並採用隨機行為者導向模型（Stochastic Actor-Oriented Model, SAOM）同時建構網絡結構演化與企業創新行為轉換。此方法能有效區分企業因相似而形成引用關係之選擇機制，與企業因引用特定知識來源而調整創新策略之影響機制，符合本研究探討網絡與行為共同演化之核心需求。
研究結果顯示，知識組合能力對企業在探索和利用創新行為轉換無直接效果，知識多樣性僅對利用型創新呈現顯著倒U型關係，技術距離對探索與利用型創新皆呈倒U型關係，網絡效率僅顯著促進探索型創新，網絡重要性則對兩類創新行為皆具一致正向效果。本研究在理論上補足動態網絡與探索與利用型創新行為轉換之關聯，在方法上展示SAOM於創新研究之適用性，在實務上亦可作為企業布局知識網絡與政府推動產業創新生態之參考。

This study investigates how a firm's internal knowledge base and its position within knowledge flow networks jointly influence the transition between exploratory and exploitative innovation. In the rapidly evolving artificial intelligence (AI) industry, technological progress relies heavily on the recombination and cross-domain integration of knowledge. Balancing the deepening of existing capabilities with the exploration of new technological domains has become a critical determinant of firms' long-term competitiveness. However, prior research has largely adopted a static perspective, paying limited attention to the co-evolution of network structures and firm-level innovation behavior. To address this gap, this study integrates the dynamic network perspective, the knowledge-based view, and the exploration–exploitation framework. We construct a firm-level knowledge citation network in the AI industry and propose a three-level analytical structure: (1) how inter-firm knowledge flows evolve over time; (2) how internal knowledge capabilities—knowledge combinatorial capacity, knowledge stock diversity, and technological distance—shape exploratory and exploitative innovation; and (3) how a firm's external network position, captured by network efficiency, influences its innovation strategy. Empirically, we use patent data from the AI industry, taking International Patent Classification (IPC) main groups as the unit of knowledge elements. The Stochastic Actor-Oriented Model (SAOM) is applied to jointly model network evolution and behavioral change, disentangling selection effects from influence effects. The expected findings suggest that knowledge combinatorial capacity positively affects both innovation types; knowledge diversity and technological distance exhibit inverted U-shaped relationships with both types; and higher network efficiency increases firms' access to heterogeneous knowledge, thereby promoting exploratory innovation. This study contributes by linking dynamic network evolution to the transition between exploration and exploitation, by demonstrating the applicability of SAOM in innovation research, and by offering guidance for firms positioning themselves within knowledge networks.

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