認同圈的量化與應用之初探

Exploring Quantification and Applications of Identification Spheres

「認同」的概念雖在政治、經濟、經營管理等學門，廣泛被使用，但多屬於質性的探討，尚無一明確的量化方法來研究此認同概念。本研究提出一量化方法，研究認同的概念，讓人與人之間的關係，透過數字，可清楚的展現彼此認同的關係。我們用習慣領域的認同圈的概念，引導說明認同函數、認同程度、認同矩陣、認同圈和不認同圈之量化與估算。並用兩個應用範例解說，人對人之間，針對某事件所形成的認同圈及其影響。範例一（見3.1.）說明如何善用認同程度和認同圈的概念，制定新的選舉規則，這規則的最大特點之一，是選民對候選人表達認同程度，不是只有黑白或零（不認同）一（認同），選民也可以表達不認同程度，在此新規則下，眾多的選舉形式可展現出不同的選舉結果，被選上的人也較能反應民意。範例二（見3.2.）說明如何善用認同矩陣，設定一個認同門檻，選出社群內最少或成本最低的成員組合（核心人物、意見領袖…），來含蓋（影響、關照）所有關係人的數學規劃。當企業要進行新理念或產品推行時，若能先與這些核心人物進行理念的溝通或產品的試用，善用他們對社群組織成員的影響力，則企業可更能有效行銷新理念或新產品。

Although the concept of identification has been studied and used in many research areas， including psychology， political science， economics， management science， etc.， there is no specific quantitative approach. In this article， we propose a quantitative approach to study the identification conception and express human identification mathematically.Based on the definition of identification sphere of Habitual Domains Theory， we define and explain the concepts of identification function， degree of identification， identification matrices， identification spheres and disidentification spheres. Using two examples we show how people form identification spheres in a given particular time and event. In Example 1(see 3.1.)， we introduce an approach and explain how to apply the notion of identification degree and sphere to election. By using this approach， voters can not only express their identification with candidates， but also disidentification with them. Different forms of election by using this new rule of election lead to different results of the election， which reflect more identification of the voters will be presented. In Example 2(see 3.2.)， we introduce a mathematical programming， utilizing identification matrices， and thresholds of identification， to find an optimal combination of key members to influence all targets in social groups. To promote effectively its products or services to its target customers， corporate could communicate with this set of key members before launching the new products or services.