從紙本地圖到智慧自駕：中小型地圖企業在資料產業轉型下的競爭策略模型研究

Abstract

從紙本地圖描繪航道與山川的古老技藝，到智慧車輛即時感知環境的高精度數據平台，地圖產業歷經數世紀的技術與應用變遷。特別是在智慧交通與自動駕駛浪潮之下，地圖早已不僅是空間資訊的載體，更成為人工智慧決策系統的中樞基礎。地圖產業從資訊供應者的角色，轉型為融合資料、生態、平台與AI運算的複合型數據產業。 本研究聚焦於「從手繪到自駕」的轉變過程中，中小型地圖企業如何在規模、資源與技術受限下，面對Google、Apple等平台巨擘所主導的產業生態，尋求生存與轉型之道。為回應此問題，本文結合資源基礎理論 (Resource-Based View, RBV)與動態能力理論 (Dynamic Capabilities Theory, DCT)，以Zenrin（日本）、Tmap Mobility（韓國）、Mapbox（美國）與勤崴國際（台灣）為研究個案，橫跨數位化、行動化、雲端API、開源社群、車聯網與AI自駕等六大技術浪潮，探討其策略演進與資源整合模式。 與既有聚焦於大型平台企業的研究不同，本研究首度針對中小型地圖企業在制度依附與封閉場域策略中的差異化行動邏輯，提出具備跨階段適配力的策略模型。研究發現，若企業能建構差異化資源（如政府合作、自有圖資、模組化平台），並展現高度的資源重構與異業整合能力，即使處於小型市場，亦能建立具競爭力的利基地位。 為統整技術與市場動態下的策略邏輯，本研究提出「資源 × 能力 × 技術階段 × 市場規模 × 使用者邏輯」(RCTMU)五策略構面矩陣，並歸納三類策略典型（品質深化型、平台閉環型、開放模組型）。本研究除彌補資料密集型產業中小企業策略演化之理論空缺外，亦提出具備高度可轉用性與模組化的策略組合架構，協助中小企業在AI與自駕浪潮中建構「小而強、小而靈活」的成長型競爭模式。

From the ancient art of hand-drawn maps to the high-definition geospatial platforms that power real-time autonomous mobility, the mapping industry has undergone centuries of technological and functional transformation. With the rise of smart mobility and autonomous driving, maps have evolved beyond static representations to become central infrastructure for AI-driven decision-making systems. The industry is no longer a mere supplier of spatial data, but a complex ecosystem integrating geospatial intelligence, cloud platforms, sensor fusion, and algorithmic services. This study investigates how small and medium-sized mapping enterprises (SMEs), constrained by limited scale, resources, and technical capacity, respond to the dominance of platform giants such as Google and Apple. To address this challenge, the study adopts the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), and analyzes four representative case studies－Zenrin (Japan), Tmap Mobility (South Korea), Mapbox (USA), and KingwayTek (Taiwan)－across six waves of technological evolution: digitization, mobile navigation, API-based cloud platforms, open-source ecosystems, connected vehicles, and AI-powered autonomy. Unlike prior research that predominantly focuses on large-scale platform firms, this study is among the first to examine how SMEs in the mapping industry formulate differentiated strategies under institutional constraints and in closed system environments. It highlights how strategic behavior varies depending on institutional embeddedness, market scale, and technological maturity, offering a cross-phase adaptable strategic model. To synthesize these insights, the study proposes the RCTMU framework, which comprises five integrated strategic dimensions: Resource configuration, Capability transformation, Technological stage, Market scale, and User role evolution. Based on this framework, three strategic archetypes are identified: quality-deepening with policy alignment, platform expansion through data closure, and open innovation via modular delivery. Beyond bridging a theoretical gap in SME adaptation within data-intensive industries, this study contributes a novel strategic toolkit that incorporates policy embeddedness as a sixth analytical dimension. The resulting framework provides actionable insights for SMEs seeking to build resilient, adaptable, and scalable business models amid platform dominance, technological turbulence, and institutional complexity.