通过引文分析和主路径分析的方法,将Web of Science (WOS)数据库中以负面口碑为主题的文献作为研究对象,研究负面口碑的重要文献与逻辑演化路径。研究发现:Richins (1983)的论文是负面口碑领域中最重要的文献之一,而负面口碑的逻辑演化有三条重要路径,分别是:负面口碑行为、负面口碑动机、抱怨和服务补救。<br>By using the methods of citation analysis and main path analysis, taking the literature with nega-tive word-of-mouth as the research object in the Web of Science (WOS) database, the important literature and logical evolution path of negative word-of-mouth have been studied. The results reveal that the paper of Richins (1983) plays the most important role in negative word-of-mouth field; and there are three important logical evolution routes: behavior of negative word-of-mouth, motive of negative word-of-mouth, complaint and service recovery.
负面口碑,主路径分析,多全局主路径,全局关键路径, Negative Word-of-Mouth Main Path Analysis Multiple Global Main Path Global Key Route基于WOS的负面口碑发展之路径分析
熊辉1,2*,黄俊健1,梁培锋1
1广东广业开元科技有限公司,广东 广州
2广州临观教育信息有限公司,广东 广州
收稿日期:2018年9月7日;录用日期:2018年9月22日;发布日期:2018年9月29日
摘 要
通过引文分析和主路径分析的方法,将Web of Science (WOS)数据库中以负面口碑为主题的文献作为研究对象,研究负面口碑的重要文献与逻辑演化路径。研究发现:Richins (1983)的论文是负面口碑领域中最重要的文献之一,而负面口碑的逻辑演化有三条重要路径,分别是:负面口碑行为、负面口碑动机、抱怨和服务补救。
第二步,WOS搜寻设定。进入WOS的进阶搜寻功能,选择all languages、all document types、all years (1947-present)并勾选SCI-EXPANDED 和SSCI两组数据库,以确保可以搜寻到数据库所有符合的样本。
第三步,布尔逻辑运算。依序输入第一步的关键词群组,得到子文献样本集合。接着,使用布尔逻辑表达式(and、or、not)精炼出负面口碑的最终文献样本集合,如图1所示,其中TS代表针对论文的Title、Abstract、Key Words、Key Words Plus进行检索,TI代表只针对论文的Title进行检索,框框旁的数字代表执行检索后得到的文献样本数量。
图1. 布尔逻辑筛选图
list of secondary keyword
指标
关键词
传播沟通
spread, disseminate, propagate, connect, link up, communication, diffusion, compliment, public praise, reputation
媒介
face to face, social media, social network, twitter, facebook, reviews, forum, blog
口碑营销
buzz, social interact, brand advocates, cause influencers, ambassador programs, viral marketing
口碑特性
tie strength, valance, incidence, strength, motivation
不满意,流言
complaint, mutter, murmur,resentful, discontent, dissatisfy, disaffect, rumor, bad news
表1. 次要关键词一览表
图1中各集合的详细说明如下。
1) 集合1:“在TS搜寻所有主要关键词”与“在TS搜寻所有次要关键词”所获得的文献样本交集。集合1中的所有文献样本,在Title或Abstract或Key Words或Key Words Plus等位置,皆同时含有主要关键词和次要关键词。
本文参考Liu、Lu (2012) [24] 建议的主路径模型,分析了78篇文献样本,绘出了负面口碑学术论文的主路径。主路径中每个节点代表一篇文献,连接线表示论文间的引用关系,箭号则表示知识流动的方向,链接线的粗细代表连接线的权重,连结线愈粗,代表该路径(引用关系)的重要性愈高。我们将使用三种主路径分析方法,从不同的观点来探讨负面口碑领域文献的发展脉络。第一种方法是以历史发展过程为主的局部主路径(Local main path);第二种是重视整体知识扩散轨迹以及子领域概况的多全局主路径(Multiple global main path);最后一种是比较多条重要路径互动关系,着眼研究领域发展结构的全局关键路径(Global key-route)。
部分负面口碑文献无法被囊括在文献样本中,主要有以下几个原因:1) 文献不在Web of Science的数据库当中;2) Web of Science数据库中,早期文献无摘要,故无法被搜寻到;3) 部分讨论负面口碑的文章,标题和摘要中没有负面口碑,故无法被搜寻到。虽然如此,本文经过熟读负面口碑文献手动增加重要文献、订定负面口碑关键词并通过布尔逻辑筛选等选样步骤,所选出的文献样本已很具代表性,但限于篇幅,并未将它们全部在参考文献中列出。
文章引用
熊辉,黄俊健,梁培锋. 基于WOS的负面口碑发展之路径分析WOS-Based Path Analysis for the Development of Negative Word-of-Mouth[J]. 数据挖掘, 2018, 08(04): 201-209. https://doi.org/10.12677/HJDM.2018.84021
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