隨著智慧製造與工業 4.0 的推動，人工智慧（Artificial Intelligence, AI）已成為製造業提升產能、良率與品質決策的核心技術。然而，電池模組產業因設備異質性高、跨站點整合複雜、資料品質參差不齊，使 AI 導入面臨更高的挑戰。既有研究多著重於 AI 技術本身之開發，較少從組織能力、資料治理與外部環境等多構面整合觀點探討 AI 導入的成功因素。因此，本研究旨在透過 TOE（Technology–Organization–Environment）模型與 AI Readiness，建構一套適用於電池模組產業的 AI 導入分析架構，並探究技術構面、環境構面如何透過組織 AI 準備度（中介機制）影響 AI 導入成效與持續導入意願。
本研究以台灣電池模組及其相關製造產業之員工為研究對象，採用問卷調查法蒐集資料，並透過 SmartPLS 4 進行 PLS-SEM 結構方程模型分析，以檢驗研究模型與八項研究假說。問卷內容包含設備連線能力、資料品質、AI 技術成熟度、產業競爭壓力、政府政策、供應鏈數位化要求、組織 AI 準備度、AI 導入成效及持續導入意願等構面。
研究主要貢獻在於：
(1) 建構一套結合 TOE 與 AI Readiness 的 AI 導入整體分析框架，補足過往研究對資料治理與組織能力面向之不足；
(2) 提供電池模組產業在 AI 導入前的診斷與評估；
(3) 為企業在設備聯網、資料治理、人員技能培訓、跨部門協作與政策因應等面向提出具體管理意涵。
本研究結果期望協助電池模組產業在面對智慧化轉型需求時，能更有效規劃 AI 導入策略，縮短從概念驗證（POC）到產線落地的落差，並強化 AI 專案的長期推動能力。

With the advancement of smart manufacturing and Industry 4.0, Artificial Intelligence (AI) has become a core technology for enhancing manufacturing productivity, yield, and decision quality. However, the battery module industry faces greater implementation challenges due to high equipment heterogeneity, complex cross-site integration, and inconsistent data quality. Prior studies focus mainly on AI technology itself, with limited attention to a multi-dimensional perspective integrating organizational capability, data governance, and the external environment. This study constructs an AI implementation framework for the battery module industry by integrating the Technology–Organization–Environment (TOE) model with the AI Readiness concept, examining how the technology and environment dimensions influence AI implementation outcomes and continuous adoption intention through organizational AI readiness as a mediating mechanism.
Targeting employees of Taiwan's battery module and related manufacturing industries, data were collected via questionnaire and analyzed using PLS-SEM with SmartPLS 4 to test the model and eight hypotheses. The questionnaire covers equipment connectivity, data quality, AI technological maturity, industry competitive pressure, government policy, supply chain digitalization requirements, organizational AI readiness, AI implementation effectiveness, and continuous adoption intention.
The main contributions are:
(1) constructing an integrated TOE–AI Readiness framework addressing prior gaps in data governance and organizational capability.
(2) providing a diagnostic tool for the industry before AI implementation.
(3) offering managerial implications on equipment connectivity, data governance, staff training, cross-departmental collaboration, and policy response. The findings help narrow the POC-to-deployment gap and strengthen long-term AI project execution.

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