本研究旨在了解消費者如何運用說服知識，來判讀線上口碑訊息之真偽。說服知識一詞，最早於1994年，由Friestad以及Wright在其著作「說服知識模型：人們如何回應他人的說服意圖」一文中所提出。該模型在當時線上口碑尚未流行的年代，是完全以線下情境為研究主題開發而成。也因此，本研究希冀打造適用於線上口碑環境所使用的線上說服知識模型，藉由深度訪談八位受訪者，了解他們使用線上口碑的情形，進而深入探討，他們如何在今日線上口碑充斥著置入性行銷口碑的環境中，運用自身的說服知識，來辨認線上口碑訊息之真偽。在受訪者分享如何辨別偽口碑的經驗談時，本研究也因此了解人們是如何運用自身對產品、議題、品牌等主題知識(topic knowledge)，如何運用說服知識(persuasion knowledge)，如何應用說服者知識(agent knowledge)等由Friestad以及Wright所歸納而成的三大知識體系，來辨別線上口碑的真實性。本研究更進一步指認出台灣線上環境所特有的”鄉民”知識，並將之融入到線上說服知識模型之中。本研究結果顯示，人們對線上口碑環境的熟悉程度(是否經常瀏覽特定網站)，以及個人涉入口碑的深淺程度(觀看線上口碑的頻繁程度)，都影響到個人線上說服知識模型之架構。本研究是線上口碑文獻中，首度建構線上說服知識模型之研究。 / This research explores consumers’ use of persuasion knowledge in judging the credibility of eWOM. Persuasion Knowledge is a concept first coined by Friestad and Wright (1994) in their work of The Persuasion Knowledge Model: How people cope with persuasion attempts. The Persuasion Knowledge Model, however, was designed specifically for the offline context. This research aims to build an online Persuasion Knowledge Model by conducting in-depth interviews to eight participants in identifying their perceived dubious eWOM triggers. Throughout the process of participants’ reasoning with these triggers, we were able to see how consumers judge the credibility of eWOM by using their persuasion knowledge, topic knowledge, and agent knowledge in the online context. The findings also suggest a fourth knowledge base—villager knowledge—in the online WOM context. Furthermore, this study shows that one’s familiarity with an online WOM environment and one’s level of involvement with online WOM all influence the construction of one’s online Persuasion Knowledge Model. This study is a first in constructing the online Persuasion Knowledge Model in the eWOM literature.
| Identifer | oai:union.ndltd.org:CHENGCHI/G0096461006 |
| Creators | 陳禹安, Chen, Yu An |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 英文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
Page generated in 0.0015 seconds