本研究旨在探討 Glassdoor 平台上的員工前景評價（員工 OUTLOOK）與企業債務成本之關聯。隨著企業經營風險和資訊環境日益複雜，傳統基於歷史數據的財務報表和管理層預測，往往存在落後性而無法完全反映企業未來的風險。員工 OUTLOOK 作為一項反映內部員工對公司未來表現判斷之微觀軟性資訊，可能在資訊不對稱的情況下，為債權人提供補充的前瞻性風險評估參考。
參考文獻 Fan et al. (2024) 之實證架構，以 2021 至 2023 年全球經濟自疫情復甦之特殊時期為研究區間，取得美國上市公司980筆年度觀察值作為研究樣本。在計量設定上，因應個別資料庫權限之客觀限制，本研究改以會計基礎總體平均債務成本作為依變數，並以平均總負債作為分母；同時，控制變數亦隨之調整。
主要實證發現表明，在控制行業與時間固定效應後，核心自變數員工 OUTLOOK 對企業債務成本（COD）呈現顯著的負向影響。此一主要實證結果，有力地支持了 Fan et al. (2024) 關於員工前瞻性揭露能緩釋市場資訊不對稱的理論觀點。橫斷面分析進一步發現，此一效果在規模較小、資訊環境較不透明的企業中更為明顯，在經濟實質上符合軟性資訊於資訊不透明企業中邊際效果較大的預期。同時，產業分群測試精準地反映出產業異質性，其調降融資成本的實質影響力主要集中於科技與軟體業，以及製造與其他產業，反映出外部債權人在面對高創新、高不確定性之知識密集型產業時，對內部軟性資訊存在更強烈的依賴。最後，敏感性測試亦呈現出兩者在有限研究區間內存在共線性或資訊重疊。
本研究在 2021 至 2023 年疫情復甦期之特定時空背景下，客觀呈現了網路平台之軟性資訊在債務定價機制中的實質影響力。本研究之發現屬關聯性研究階段並不具備高度的因果關係證明，但其主要實證結果與基準文獻高度吻合，應能為外部投資人與金融機構在評估企業前瞻性營運風險時，提供具備時空脈絡的微觀參考依據。

This study investigates the relationship between online employee business outlook disclosures (employee OUTLOOK) on Glassdoor and the cost of corporate debt. Traditional, historically-based financial statements and managerial forecasts often suffer from information lag and fail to fully capture future corporate risks. As a micro-level, soft-information signal reflecting internal employees' collective judgments, employee OUTLOOK provides creditors with supplementary, forward-looking risk assessment references under conditions of information asymmetry. Drawing upon the empirical framework of the baseline study by Fan et al. (2024), this study focuses on the post-pandemic economic recovery from 2021 to 2023, utilizing 980 firm-year observations from U.S. S&P 500 companies. Due to database constraints, this study utilizes the accounting-based average cost of debt (COD) as the dependent variable. The primary empirical findings indicate that after controlling for industry and year fixed effects, employee OUTLOOK has a statistically significant negative impact on the cost of corporate debt. Cross-sectional analyses further reveal that this mitigating effect is more pronounced in smaller firms where the information environment is less transparent, consistent with the economic intuition that soft information yields higher incremental value in information-opaque environments. Furthermore, industry subsample tests demonstrate that the impact is concentrated within the technology and software sectors, as well as manufacturing industries, reflecting creditors' stronger reliance on internal soft information when evaluating knowledge-intensive or highly uncertain firms. Finally, sensitivity checks suggest potential multicollinearity or information overlap with overall ratings within the limited sample window.

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