隨著社群媒體成為企業重要的數位行銷工具，品牌資訊得以透過網絡快速傳播，並可能影響消費者之產品認知與購買決策。然而，對於採用企業對企業對消費者（Business-to-Business-to-Consumer， B2B2C）銷售模式之企業而言，品牌並未直接向終端消費者完成交易，社群互動是否能進一步與企業營收形成穩定關聯，仍有待實證檢驗。因此，本研究以六度分隔理論（Six Degrees of Separation）與弱連結理論（The Strength of Weak Ties）為理論基礎，以C公司台灣分公司為研究個案，探討Facebook社群互動、資訊擴散與企業營收之關聯。 本研究蒐集2023年1月至2025年12月共36個月之Facebook官方粉絲專頁後台數據與公司內部月營收資料，並另取得2026年1月營收資料，以進行一期滯後分析。研究變數包括分享數、留言數、曝光數、發文篇數、傳播轉換率、當月營收及次月營收。本研究採用描述性統計、標準化趨勢比較、Pearson相關分析、月變化量分析、當月與次月多元線性迴歸分析，並以成對樣本t檢定比較不同內容類型之傳播轉換率差異。 研究結果顯示，Facebook分享數、留言數、曝光數及發文篇數與當月營收之相關程度均偏低，且皆未達統計顯著水準。當月多元迴歸模型之R平方為0.0774，整體模型亦未達顯著。月變化量分析同樣未發現社群互動增減與營收增減之間存在顯著且穩定的線性關聯。 在一期滯後分析方面，分享數與曝光數和次月營收之相關係數雖較當月提高，但留言數及發文篇數未呈現一致結果，且各項相關均未達統計顯著。次月多元迴歸模型之R平方為0.0656，低於當月模型，整體模型亦未達顯著。因此，尚無足夠證據支持Facebook社群互動指標與次月營收之關聯程度高於其與當月營收之關聯程度。 在強連結與弱連結比較方面，本研究以分享數作為弱連結擴散之代理指標，以留言數作為相對較具互動深度之強連結代理指標。分析結果顯示，分享數與營收之相關係數絕對值低於留言數，且兩者均未達統計顯著，因此無法證明弱連結擴散代理指標與營收之關聯程度高於強連結互動代理指標。 在內容類型分析方面，情感／形象型內容之平均傳播轉換率高於資訊型內容，但兩類內容之差異未達統計顯著；資訊型內容與營收之相關程度在數值上高於情感／形象型內容，但兩者均未達統計顯著。整體而言，本研究所提出之主要假說均未獲統計支持。 綜合研究結果可知，在C公司B2B2C銷售模式下，Facebook社群互動與企業月營收之直接線性關聯有限。標準化趨勢比較與標準化多元迴歸結果亦未發現社群互動與營收之間存在穩定且顯著的線性關聯。此一結果並不表示社群媒體不具管理價值，而是顯示社群資訊擴散與企業營收轉化分屬不同層次之績效問題，兩者之間仍受到消費者決策、促銷活動、產品組合、通路訂貨、出貨安排及營收認列時點等因素影響。 六度分隔理論與弱連結理論仍可作為解釋品牌資訊跨圈層傳播之理論基礎，但本研究之月彙總資料尚不足以驗證完整網絡路徑，亦無法證明社群擴散能穩定轉化為企業營收。基於研究結果，B2B2C企業除持續衡量社群曝光與互動外，亦可透過產品體驗、使用情境分享、消費者推薦及通路合作活動，鼓勵實際使用者將產品經驗傳遞至其家人、朋友及其他社交圈層，以強化人際網絡中的口碑擴散，並建立社群互動、通路活動與後續銷售資料之追蹤機制。本研究結果可供B2B2C企業進行社群績效衡量、內容策略規劃、口碑推薦機制及通路資料整合之參考。;As social media has become an important digital marketing tool， brand information can be rapidly disseminated through online networks and may influence consumers’ product awareness and purchase decisions. However， for firms operating under a Business-to-Business-to-Consumer (B2B2C) sales model， brands do not directly complete transactions with end consumers. Whether social media engagement can be translated into a stable relationship with corporate revenue therefore requires further empirical examination. Drawing on the Six Degrees of Separation Theory and the Strength of Weak Ties Theory， this study investigates the relationships among Facebook engagement， information diffusion， and corporate revenue， using the Taiwan branch of Company C as a case study. This study collected 36 months of Facebook Page Insights data and internal monthly revenue records from January 2023 to December 2025. Revenue data for January 2026 were additionally obtained for the one-period lag analysis. The variables examined included shares， comments， impressions， number of posts， diffusion conversion rate， current-month revenue， and next-month revenue. Considering the differences in measurement units and numerical scales between social media engagement indicators and revenue， the main social media engagement indicators and revenue variables were transformed into standardized Z-scores for trend comparison and standardized multiple regression analyses. The empirical analyses subsequently included descriptive statistics， Pearson correlation analysis， month-to-month change analysis， current-month and next-month multiple linear regression analyses， and a paired-samples t-test to compare the diffusion conversion rates of different content types. The results indicate that Facebook shares， comments， impressions， and number of posts had only weak relationships with current-month revenue， and none of these relationships reached statistical significance. The current-month multiple regression model produced an R-squared value of 0.0774 and was not statistically significant. The month-to-month change analysis also failed to identify a significant and stable linear relationship between changes in social media engagement and changes in revenue. In the one-period lag analysis， the correlations of shares and impressions with next-month revenue were numerically higher than their correlations with current-month revenue. However， comments and number of posts did not show the same pattern， and none of the next-month correlations reached statistical significance. The next-month multiple regression model yielded an R-squared value of 0.0656， which was lower than that of the current-month model， and the overall model was not statistically significant. Therefore， there was insufficient evidence to support the proposition that Facebook engagement indicators were more strongly related to next-month revenue than to current-month revenue. For the comparison between strong- and weak-tie proxies， shares were used as a proxy for weak-tie diffusion， while comments were used as a proxy for relatively deeper interaction. The absolute correlation between shares and revenue was lower than that between comments and revenue， and neither relationship was statistically significant. Accordingly， the findings did not demonstrate that the weak-tie diffusion proxy had a stronger relationship with revenue than the strong-tie interaction proxy. Regarding content types， emotional and brand-image content generated a higher average diffusion conversion rate than informational content， but the difference was not statistically significant. Informational content showed a numerically stronger relationship with revenue than emotional and brand-image content， but neither relationship reached statistical significance. Overall， the major hypotheses proposed in this study were not statistically supported. The findings suggest that， under Company C’s B2B2C sales model， the direct linear relationship between Facebook engagement and monthly corporate revenue is limited. The standardized trend comparisons and standardized multiple regression analyses likewise did not identify a stable and statistically significant linear relationship between social media engagement and revenue. This result does not imply that social media lacks managerial value. Rather， it indicates that social media diffusion and revenue conversion represent different levels of performance and are jointly affected by consumer decision-making， promotional activities， product mix， channel ordering， shipment arrangements， and revenue recognition timing. The Six Degrees of Separation Theory and the Strength of Weak Ties Theory may still serve as theoretical perspectives for explaining the cross-group diffusion of brand information. Nevertheless， the monthly aggregate data used in this study were insufficient to verify complete network paths or demonstrate that social media diffusion could be consistently translated into corporate revenue. Based on the findings， B2B2C firms may use product trial programs， usage-scenario sharing， consumer referral activities， and coordinated channel campaigns to encourage actual users to share their product experiences with family members， friends， and other social groups， thereby strengthening interpersonal word-of-mouth diffusion. Firms should also establish tracking mechanisms that integrate social media engagement， channel activities， and subsequent sales data. The findings provide practical references for social media performance measurement， content strategy planning， word-of-mouth referral mechanisms， and channel data integration in B2B2C firms.
