在全球氣候變遷與永續發展議題日益受到重視的背景下，企業如何透過科技創新提升環境績效，已成為學術與實務界關注的重要議題。人工智慧（AI）因具備資料分析、預測與決策支援等能力，被視為促進企業資源效率與環境管理的重要工具。然而，企業是否採用AI技術往往受到多種內外部因素影響，尤其是利害關係人壓力與企業自身能力。本研究以台灣企業為研究對象，探討利害關係人壓力與AI核心能力如何影響企業AI採用，並進一步分析AI採用對企業環境績效的影響機制。
本研究建構以「利害關係人壓力—AI核心能力—AI採用—環境績效」為主軸之研究架構。利害關係人壓力包含顧客壓力、員工壓力、股東壓力與政策壓力；AI核心能力則包括未來預測分析能力與策略最佳化能力。並進一步檢驗AI採用意願對採用程度與環境績效之影響，以及AI採用程度在採用意願與環境績效之間的中介效果。
在研究方法上，本研究採用問卷調查法進行資料蒐集，於2025年4月至5月透過線上問卷方式蒐集資料，共取得159份有效樣本。研究資料透過統計分析進行信度與效度檢驗，並採用迴歸分析檢驗各研究假設。研究結果顯示：(1)利害關係人壓力對企業AI採用意願具有顯著正向影響；(2)AI核心能力能顯著提升企業AI採用意願；(3)AI採用意願能進一步提升企業AI採用程度與環境績效；(4)AI採用程度在AI採用意願與環境績效之間具有完全中介效果。此外，本研究所提出之十項研究假設皆獲得實證支持。
本研究在學術上整合利害關係人理論與AI採用研究，提出影響企業AI採用與環境績效之整合性分析架構；在實務上則提供企業管理者重要啟示，即企業若欲透過AI技術提升環境績效，不僅需回應利害關係人對永續發展的要求，亦需培養AI相關分析與決策能力，並將AI技術實際導入營運流程，以發揮其對環境績效改善之效益。

In recent years, climate change and sustainable development have become critical global issues. Firms are increasingly expected to improve environmental performance while maintaining operational efficiency and competitiveness. Artificial intelligence (AI), with its capabilities in data analytics, prediction, and decision support, has been widely recognized as a key technology that can assist organizations in optimizing resource utilization and enhancing environmental management. However, the adoption of AI technologies by firms is influenced by multiple internal and external factors, particularly stakeholder pressure and organizational capabilities. Therefore, this study investigates how stakeholder pressure and AI core capabilities influence AI adoption and subsequently affect environmental performance in Taiwanese enterprises.
This research develops an integrated framework linking stakeholder pressure, AI core capabilities, AI adoption, and environmental performance. Stakeholder pressure includes customer pressure, employee pressure, shareholder pressure, and policy pressure, while AI core capabilities consist of predictive analytics capability and strategic optimization capability. The study further examines the influence of AI adoption intention on the level of AI adoption and environmental performance, as well as the mediating role of AI adoption level between adoption intention and environmental performance.
Data were collected through an online questionnaire survey conducted from April to May 2025, yielding 159 valid responses from Taiwanese enterprises. Statistical analyses including reliability and validity tests and regression analysis were conducted to examine the proposed hypotheses.
The empirical results indicate that: (1) stakeholder pressure has a significant positive effect on firms’ AI adoption intention; (2) AI core capabilities significantly enhance AI adoption intention; (3) AI adoption intention positively influences both the level of AI adoption and environmental performance; and (4) the level of AI adoption fully mediates the relationship between AI adoption intention and environmental performance. All ten hypotheses proposed in this study are supported by the empirical findings.
This study contributes to the literature by integrating stakeholder pressure and AI capability perspectives to explain firms’ AI adoption and environmental performance. From a managerial perspective, the findings suggest that firms aiming to enhance environmental performance through AI should not only respond to stakeholder expectations for sustainability but also develop AI-related analytical and decision-making capabilities and effectively implement AI technologies in their operational processes.

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