本研究以創新抵制理論（Innovation Resistance Theory, IRT）為基礎，探討台灣中小企業在人工智慧（AI）導入歷程中，常見的導入失敗現象與內部抵制成因。儘管多數企業已展開AI技術轉型，實際成功落地者比例仍低，導入過程中普遍面臨「失效」（技術無效）與「失靈」（組織接不上）雙重挑戰。
本研究透過問卷調查與結構方程模型（SEM）分析後，發現「價值障礙」、「傳統障礙」與「形象障礙」會顯著提升員工對AI技術的心理抗拒，進而透過「創新抵制行為」此一中介變數，對導入績效產生負向影響，此研究結果顯示員工心理抗拒的行為表現為績效落差的關鍵機制。相對地，「使用障礙」與「風險障礙」對抵制與績效皆未產生顯著影響，反映AI技術導入企業的瓶頸主要非技術本身，而是組織價值與文化落差。
進一步檢驗其對「創新抵制影響導入績效」路徑之調節效果，發現性別不具調節效果，這表示傳統企業轉型過程中常見的性別數位落差影響已趨微弱。另外，工作年資具正向調節效果，表示資深員工可因其經驗與信任資本緩解抵制對績效的衝擊；此外，在研究的結果中亦發現一個有趣的現象，公司營收規模則呈現負向調節，這表示中小企業因組織較扁平與應變快速，在相同抵制程度下反而表現出較佳績效。
為了補充量化分析之詮釋力，本研究另外進行個案分析，透過三家異質性企業進行訪談與分析，個案分析中發現，一間企業能成功導入AI技術的關鍵在於文化支持與制度配套，而非單純依賴技術成熟度，如果具備完整的組織溝通機制、充分獲得管理者支持與教育訓練的企業，亦能有效降低員工威脅感並提升參與度；反之，階層分明與依賴外部導入的企業，則常因文化摩擦與溝通失衡導致導入失敗。
本研究提出「AI技術導入阻力轉化模型」，主張企業應由單點部署邏輯轉向系統整合思維，從中介機制的掌握與調節因子的建構著手，實現技術與績效的協同進化，亦可補足AI技術導入失敗因素的理論闕漏，更提供企業面對採用AI技術導入過程中的預警與調適功能導入評估架構，協助中小企業有效因應轉型挑戰，避免科技投資成為治理風險。

Grounded in Innovation Resistance Theory (IRT), this study investigates the prevalent causes of failure and internal resistance during AI implementation in Taiwanese small and medium-sized enterprises (SMEs). Despite widespread AI adoption efforts, the success rate of effective integration remains low, as organizations commonly face the dual challenges of technical inefficacy and organizational misalignment. Using survey data and structural equation modeling (SEM), the study identifies that value barriers, tradition barriers, and image barriers significantly intensify psychological resistance toward AI among employees. This resistance, mediated through innovation resistance behavior, negatively impacts implementation performance. In contrast, usage and risk barriers show no significant influence, suggesting that implementation bottlenecks are more rooted in organizational values and culture than in technological maturity. Moderation analysis reveals that gender does not significantly alter the resistance-performance relationship, indicating a diminishing digital gender divide. However, work tenure exerts a positive moderating effect, implying that senior employees mitigate resistance impacts through accumulated trust and experience. Conversely, firm size demonstrates a negative moderating effect, suggesting that flatter, more agile SMEs outperform larger firms under comparable levels of resistance. To supplement quantitative findings, case studies of three heterogeneous firms were conducted. Results highlight that successful AI implementation hinges more on cultural support and institutional alignment than on technical readiness alone. Firms with strong communication frameworks, managerial backing, and sufficient training showed higher employee engagement and lower threat perception, while those with rigid hierarchies and externally driven initiatives faced greater implementation failure due to cultural frictions. This study proposes an "AI Resistance Transformation Model," advocating a shift from isolated deployment to integrated systems thinking. By addressing mediating mechanisms and contextual moderators, the model aims to facilitate the co-evolution of technology and organizational performance. The findings not only contribute to theory by filling gaps in understanding AI implementation failure but also offer a practical assessment framework for SMEs to preempt resistance and navigate digital transformation more effectively.

論文審定書 i
摘要 ii
Abstract iii
圖目錄 vi
表目錄 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與問題 9
第三節 研究流程 11
第二章 文獻探討 13
第一節 產業下的人工智慧的概況 13
第二節 創新抵制理論（Innovation Resistance Theory） 14
第三節 AI技術導入績效(Performance of AI implementation) 22
第三章 研究方法 26
第一節 量化研究架構 27
第二節 量化研究的變數操作型定義與問卷 29
第三節 個案研究與訪談設計 33
第四章 研究結果 40
第一節 量化研究的樣本敘述與信效度分析 40
第二節 假說驗證 47
第三節 量化研究的結果 51
第四節 質性訪談 60
第五節 質性訪談結果 65
第六節 個案實證下的模型驗證與調整觀察 66
第五章 結論與建議 68
第一節 研究結論 68
第二節 管理意涵 71
第三節 研究限制 78
參考文獻 80
中文部分 80
英文部分 80
網路資料 85
附件一 問卷內容 86

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