一、決定初始核心(initial core)文獻與資料庫檢索
本研究資料庫檢索是使用ISI Web of knowledge，將初始核心 14 篇 文獻之參考文獻作為雪球式引文研究的第一波資料，一共有897 筆。將這 897 筆資料進行檢驗與篩選(interpretation and validation)，去掉 ISI Web of knowledge 中無法取得的資料1、非學術性雜誌刊物或非SSCI 領域資料2， 經過這些整理匯總與交叉比對的過程，對於篇名、年代和出版刊物的校正 以及不重覆計入同一筆資料，最後剩下253 篇文獻。
再用第一波資料之253 篇文獻進行第二波文獻檢索，此資料不同於第 一波資料，而為會隨檢索期間變動的動態資料，第二波資料檢索的期間為 2008 年 11 月 4 日至 10 日，合計被引文次數為 77318 篇。
二、文獻共被引配對
依上述的結果將其進行書目檔案整理，以一對一的方式進行共引次數 的配對，並且將結果編制成行列相同之文獻的共被引矩陣。矩陣內的數 值，表示第三方文獻同時引用兩指定文獻為參考書目的次數，稱為共被引 次數(co-citation frequency)。接著分別計算文獻之總被引次數(total citation, TC)與總共被引次數(total co-citation, TCC)，將總被引次數(TC) 除以發表至今的年數算出平均總被引次數，將平均總被引次數與總共被引 次數同時排名，列舉出前50 篇高度被引用的文獻作為本研究之觀察樣本 群；選取50 篇樣本的理由在於，經過多次的嘗試之後，選取 50 篇文獻的
1 ISI Web of knowledge 中無法取得的資料包括 Working paper、部分未於 SSCI 期刊
發表研討會論文集之論文、博士學位論文、書籍參考資料、不是SSCI 期刊收錄的資料
(如 Industrial and Corporate Change 於 2002 年後才是 SSCI 之一、Economics and Organization 於 1989 年後才進入 SSCI……等等)，且此資料庫的授權目前只能取得 1956 年以後的資料，以上為本研究資料取得的限制，但並不足以影響主要的研究結果。
2 非學術性雜誌刊物或非 SSCI 領域資料(如：IEEE、Physics)經資料庫蒐所結果發現其
引文次數皆非常低，且領域探討的議題與理論與本研究較無關，非本文討論的對象。
結果可同時兼顧研究領域中文獻的同質性與易辨識性，因而能呈現此領域 知識架構。為了避免選取之研究對象與本研究領域不同，因而影響最終結 果之呈現，本研究分別就選定之文獻進行了期刊、題目、摘要以及文獻共 被引次數等相關資訊之確認，最後結果如表3.3 和表 3.4 所示。(表 3.3 為 列舉平均TC 與 TCC 排名前 50 篇之文獻，表 3.4 為其共被引矩陣) 隨後，再透過 SPSS 套裝軟體的應用，將原文獻共被引矩陣轉換為 Pearson 相關矩陣，其結果就是本研究因素分析、集群分析以及多維尺度 法等多變量統計分析之基礎(McCain, 1990)。
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(表 3.4 共被引矩陣)
三、矩陣轉換
McCain(1990)指出 Pearson correlation 是共被引研究主要之衡量基 礎。因此，我們可以透過SPSS 套裝軟體的應用，將原始共被引矩陣 (co-citation matrix)轉變為相關矩陣(correlation matrix)。使用相關係數作 為衡量基礎，主要優點有三：第一，矩陣中數值，並不只是顯示配對目標 共被引(co-cited)的絕對次數(即行或列次數的計算)，而是在說明兩者間引 用檔案(co-citation files)的相似性程度。意即，當第三方文獻經常共引用兩 目標文獻，且引用次數明顯高於其他目標文獻時，則兩文獻將被視為具有 高度正相關，同時就後續引用者的認知而言，可以稱兩目標是具有相關 (related)或是相似(similar)的關係。第二，相關係數的運用將可排除文獻之 間具有高度被引用以及具有相似檔案，但整體卻只有較少被引用次數等的 尺度(scale)不同的情形(McCain, 1990)，即標準化動作(Rowlans, 1999)。
最後，相關係數的運用可下降矩陣中數值0 的存在，以避免統計方法使用 上的問題(Rowlans, 1999)。