近年來，人工智慧（Artificial Intelligence, AI）技術快速發展，並逐漸被導入政府各項業務與公共服務之中，成為推動智慧政府與公共服務創新的重要工具。然而，政府導入AI創新服務所創造之價值，並非僅取決於技術本身是否成熟，更涉及民眾是否願意採用、公務人員是否能有效運用，以及服務是否真正回應使用者需求。因此，如何從內外部顧客觀點評估政府AI創新服務之效益與價值，已成為值得探討的重要議題。
本研究以公部門服務價值鏈（Public Sector Service Value Chain, PSSVC）作為整體研究觀點，以勞動部勞動力發展署桃竹苗分署及臺中市政府就業服務處所推動之AI虛擬面試系統為研究個案，探討影響民眾使用政府AI創新服務之因素，並分析公務人員與求職者對AI虛擬面試系統之評價與看法。本研究採混合研究方法進行分析，量化研究部分以整合型科技接受模型（UTAUT）為基礎，並納入工作相關性、知覺愉悅、科技信任及政府信任等變項建構研究架構，透過問卷調查蒐集301份有效樣本，並以偏最小平方法結構方程模型（PLS-SEM）進行分析；質性研究部分則透過深度訪談方式，蒐集求職者及公務人員之實際使用經驗與看法。
研究結果發現，在影響民眾使用意圖之因素方面，績效期望、促成條件、工作相關性及知覺愉悅對使用意圖具有顯著正向影響，其中以知覺愉悅之影響效果最為明顯；努力期望、社會影響、科技信任及政府信任則未達顯著水準。質性訪談結果顯示，多數求職者認為AI虛擬面試系統有助於熟悉面試流程、降低面試焦慮及提升求職準備效率，並對系統抱持正面評價，但使用者滿意度仍取決於回饋內容是否具有個人化與實用性。另一方面，公務人員普遍認為AI虛擬面試系統有助於提升服務量能、減少重複性工作及強化求職輔導品質，惟在推動過程中仍面臨設備維護、前端引導、數位落差及系統限制等挑戰。
最後，本研究依據研究結果提出八項建議：第一，強化AI虛擬面試系統之回饋深度與職務客製化程度，以提升民眾使用體驗與滿意度；第二，建立AI系統與真人就業服務之銜接機制；第三，改善系統操作流程、穩定性與使用者介面設計；第四，重強化第一線人員AI應用能力與服務轉譯角色；第五，建立中央統籌、地方執行與跨機關共享之推動模式；第六，重視不同族群之數位落差與特殊需求；第七，建立使用回饋與成效評估機制；第八，提升資料治理透明度與民眾信任基礎。

In recent years, Artificial Intelligence (AI) has been increasingly adopted in government operations and public service delivery, becoming an important tool for promoting smart government and public service innovation. However, the value created by AI-enabled public services depends not only on the maturity of the technology itself, but also on citizens’ willingness to adopt such services, public servants’ ability to effectively utilize them, and the extent to which these services can respond to users’ needs. Therefore, evaluating the effectiveness and value of government AI innovation services from both internal and external customer perspectives has become an important research issue.
This study adopts the Public Sector Service Value Chain (PSSVC) as its analytical framework and examines the AI Virtual Interview Systems implemented by the Taoyuan-Hsinchu-Miaoli Branch of the Workforce Development Agency, Ministry of Labor, and the Employment Service Office of the Taichung City Government. The study aims to explore the factors influencing citizens’ adoption of government AI innovation services and to investigate how job seekers and public servants perceive and evaluate these systems. A mixed-methods research design was employed. The quantitative component was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and incorporated additional variables, including job relevance, perceived enjoyment, trust in technology, and trust in government. A total of 301 valid questionnaires were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The qualitative component consisted of in-depth interviews with job seekers and public servants to gain a deeper understanding of their experiences and perceptions regarding the AI Virtual Interview System.
The findings indicate that performance expectancy, facilitating conditions, job relevance, and perceived enjoyment have significant positive effects on citizens’ behavioral intention to use the AI Virtual Interview System, with perceived enjoyment exhibiting the strongest effect. In contrast, effort expectancy, social influence, trust in technology, and trust in government do not significantly influence behavioral intention. The qualitative findings reveal that most job seekers believe the AI Virtual Interview System helps them become familiar with interview procedures, reduce interview anxiety, and improve interview preparation. Overall, participants expressed positive attitudes toward the system; however, user satisfaction largely depends on the degree of personalization and practical usefulness of the feedback provided. From the perspective of public servants, the system is perceived as enhancing service capacity, reducing repetitive tasks, and improving the quality of employment counseling. Nevertheless, challenges remain, including equipment maintenance, user guidance, the digital divide, and system limitations.
Based on the findings, this study proposes eight policy recommendations: (1) enhancing the depth of feedback and job-specific customization of the AI Virtual Interview System; (2) establishing effective coordination mechanisms between AI systems and human employment services; (3) improving system operation processes, stability, and user interface design; (4) strengthening frontline public servants’ AI competencies and their role in translating AI-generated information into service delivery; (5) developing a governance model featuring central coordination, local implementation, and inter-agency collaboration; (6) addressing the digital divide and the needs of diverse user groups; (7) establishing user feedback and performance evaluation mechanisms; and (8) enhancing transparency in data governance and strengthening public trust.

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