渴望延遲閉合需求對創意廣告效果之影響

The Influence of Desire Postpone Closure on Creative Advertising Effect

方菁容(Ching-Jung Fang)；衛萬里(Wan-Li Wei)；鄧成連(Cheng-Lein Teng)

渴望延遲閉合需求 ； 認知閉合需求 ； 廣告創意 ； 廣告效果 ； Need for Cognitive Closure (NFC) ； Desire Postpone Closure (DPC) ； Advertising Creativity ； Product Involvement ； Advertising Effect

設計學報

20卷1期（2015 / 03 / 01）

69 - 85

繁體中文

一則被專家肯定過的創意廣告其訊息內容的表達形式具有獨特的編碼手法，惟對於消費者是否具宣傳影響力，則值得後續研究探討。本研究依據渴望延遲閉合需求（Desire Postpone Closure, DPC）具有資訊處理持久性的特質、產品涉入程度對品牌產生不同關注水準，以及廣告創意是擴散性（divergence）與相關性（relevance）相乘的交互關係（D×R），瞭解三項因子與創意廣告效果對觀者之影響。研究分析以多變量變異數分析（Multivariate Analysis of Variance, MANOVA）探討產品涉入與創意品質調節廣告效果的影響；LISREL結構方程模式（Structural Equation Modeling, SEM）探討品牌知曉與學習廣告主張之中介影響。研究實證結果得知：渴望延遲閉合無法正向影響廣告效果，產品涉入程度調節渴望延遲閉合則對廣告效果產生顯著影響；同時，廣告創意品質調節渴望延遲閉合對廣告效果產生顯著影響、渴望延遲閉合透過品牌知曉對間接廣告效果產生顯著影響，且渴望延遲閉合透過學習廣告主張無法對間接廣告效果產生顯著影響。

Although the content of a message in a creative advertisement with experts' recommendation is expressed by a unique encoding process, its impact toward customers is worthy of further investigation. In this paper, we explored the influence of creative advertisement based on three factors: the permanence characteristics of Desire Postpone Closure (DPC) on information process, different levels of branding learning corresponding to the extent of product involvement, and multiplicative relationship between divergence and relevance in the advertisement (D × R). With the multivariate analysis of variance (MANOVA), we investigated the influence of product Involvement and advertising effect. On the other hand, with the structural equation modeling (SEM) of LISREL, we analyzed the mediation effects within brand awareness and Ad claim learning. This study shows that DPC does not have positive influences on the effect of advertisement, and product involvement would adjust the influence of DPC. Advertising creativity has significant influence toward advertising effect, and DPC has an indirect effect through brand awareness as well. Furthermore, DPC is not influenced by the indirect advertising effect through Ad claim learning.

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