生成式 AI 進入媒體產業後，「技術能否改善工作者處境」成為實務與學術共同關注的問題。過往研究多從效率或採用意願切入，較少處理 AI 對從業者心理感受的影響，也少有研究把媒體工作最典型的多工情境納入考量。本研究以台灣媒體與內容產業的多工工作者為對象，檢視 AI 態度與 AI 使用行為如何經由自我效能影響職場幸福感，並檢驗組織資源與多工強度的調節效果。

研究採線上問卷調查，回收 255 份，有效樣本 241 份。職場幸福感以德行職場幸福感量表（EWWS）測量，多工工作則依因素分析結果拆分為「多工負荷」與「多工調度」兩個構念。資料以多元迴歸、階層迴歸與拔靴法（5,000 次）進行分析。

結果顯示，正向的 AI 態度能顯著預測自我效能（β = .390, p < .001），AI 使用程度則不具預測力（β = −.023, n.s.）；自我效能與職場幸福感的關聯是全模型中最強的一組（r = .517, p < .001）。中介分析顯示，自我效能在 AI 態度與職場幸福感之間具部分中介效果（間接效果 = .159, 95% CI [.085, .248]），但在 AI 使用路徑上未成立。組織資源與多工強度的調節假設全數未獲支持，組織資源僅對職場幸福感有直接效果。此外，探索性分析發現多工調度能顯著提升自我效能（β = .325, p < .001），多工負荷則與各效標變項均無顯著關聯。

本研究以自我決定理論詮釋上述結果：決定 AI 能否轉化為心理資源的並非使用量，而是使用行為背後的動機品質；自我效能可視為勝任需求獲得滿足時的認知展現，多工調度則反映自主需求的滿足，而多工負荷所對應的需求受挫，理論上不會顯現在本研究所採用的正向效標上。研究並據此提出多工二元架構，區辨主動調度與被動負荷在心理機制上的差異，補充 JD-R 與 UTAUT 在高多工、資源匱乏場景下的適用邊界。實務上，本研究建議媒體組織將導入重心從「擴大使用」轉向「建立正向認知與任務自主性」。

The spread of generative AI through the media industry has raised a question that matters to both practitioners and researchers: does the technology actually improve working conditions? Existing studies have largely examined efficiency gains or adoption intentions, paying less attention to how AI affects workers' psychological experience, and rarely accounting for the multitasking conditions that define media work. This study examines how AI attitudes and AI use influence workplace well-being through self-efficacy among multitasking media workers in Taiwan, and tests the moderating roles of organizational resources and multitasking intensity.

Data were collected through an online survey, yielding 241 valid responses from 255 returns. Workplace well-being was measured with the Eudaimonic Workplace Well-Being Scale (EWWS), and multitasking was separated by factor analysis into two constructs: multitasking load and multitasking scheduling. Multiple regression, hierarchical regression, and bootstrapping (5,000 resamples) were used for analysis.

Positive AI attitudes significantly predicted self-efficacy (β = .390, p < .001), whereas the extent of AI use did not (β = −.023, n.s.). The association between self-efficacy and workplace well-being was the strongest in the model (r = .517, p < .001). Self-efficacy partially mediated the relationship between AI attitudes and workplace well-being (indirect effect = .159, 95% CI [.085, .248]), but no mediation was found along the AI use path. None of the moderation hypotheses were supported; organizational resources showed only a direct effect on well-being. Exploratory analysis further revealed that multitasking scheduling significantly predicted self-efficacy (β = .325, p < .001), while multitasking load was unrelated to any outcome.

Drawing on self-determination theory, the study argues that what turns AI into a psychological resource is not the volume of use but the quality of motivation behind it. Self-efficacy is interpreted as the cognitive expression of competence-need satisfaction, and multitasking scheduling as autonomy-need satisfaction, whereas the need frustration associated with multitasking load would not surface in the positively valenced outcomes used here. The proposed dual-construct view of multitasking distinguishes active scheduling from passive load, and clarifies the boundary conditions of JD-R and UTAUT in resource-constrained, high-multitasking settings. Practically, the findings suggest that media organizations should shift their focus from expanding AI use toward cultivating positive appraisals and task autonomy.

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