演化式類神經網路為基底的企業危機診斷模型︰智慧資本之應用

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林文修(Wen-Shiu Lin) 查詢紙本館藏

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資訊管理學系

論文名稱

演化式類神經網路為基底的企業危機診斷模型︰智慧資本之應用

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摘要(中)

危機企業的存在，整體而言是一種社會資源的誤用，從另一個角度看則正顯露出現行診斷模型的不足。事實上，企業危機診斷模型的建立，一直缺乏足夠的嚴謹性與理論基礎。這種現象導致研究模型缺乏豐富而深刻的內涵，而且過度簡化而忽略理論性辯證的研究結果，可能導致錯誤的結論。此外，之前建模工具多採用嚴謹數學與統計方法，但它們有需要許多前提假設與對資料型態有所限制的特性，從近年層出不窮的企業危機案例中，透露出它們的盲點與缺失。因此，本研究從企業經營風險、企業經營危機、智慧資本(intellectual capital)等理論，分析其內涵以及推導它們之間的關係，發現它們是企業績效評估與生存的良好解釋變數，而且各理論之間具有某種實質關係。但是各理論往往優劣互見，因此本研究加以整合，建立了新的企業危機診斷理論模型。
本研究以遺傳演算法的演化能力，系統性與自動化的解決類神經網路架構與參數的設定問題，整合成為「演化式類神經網路模式」，並成功的應用至本研究之企業危機診斷模型中。本研究另外選擇了多元區別分析、Logit迴歸模型，以及倒傳遞類神經網路當作績效評比的基準。實證結果發現，演化式類神經網路不管在學習樣本、測試樣本或整體樣本之分類正確率與穩定性上，皆比多元區別分析、Logit模式與倒傳遞類神經網路，具有較佳之模式績效。此外，在模式的概化能力上，亦是演化式類神經網路型表現較好。以財務與預測技術領域而言，能有較佳的分類與預測準確率、模式的穩定性，已是好的建模技術。換言之，本研究在乎它的概化能力與實用性。準此，演化式類神經網路模式無疑的，是企業危機診斷模型適宜的建模技術。
另一方面，本研究導入智慧資本後所建構的模型，經由第二階段實驗結果，發現智慧資本確實對企業危機診斷具增額解釋能力。亦即第二階段的實驗發現，導入智慧資本之後的模型績效評比，以使用多元區別分析與Logit迴歸模式的增額解釋能力最多。而演化式類神經網路與倒傳遞類神經網路，在智慧資本加入後的表現卻出現分歧現象，亦即部份時點的樣本在智慧資本的加入後，反而造成模型績效略降的現象。綜和而言，演化式類神經網路模式在整體分類正確率以及測試階段的分類正確率，比其他建模技術有較佳之績效。雖然，經過統計檢定分析，部份實驗之兩階段實驗績效比較，未達顯著差異，亦即部份實驗不能證實智慧資本對模型具有貢獻。但是以預測技術與財務領域的特性觀點，它們不若問卷調查或實驗室研究法之資料來源特性，是那麼的依賴統計檢定以保障研究品質。況且加入智慧資本的模型，能增進2.7%至10.86%的分類準確度，對企業危機診斷模型的本體而言，智慧資本已具有相當程度的貢獻。
總之，本研究從創新而有趣的研究觀點出發，檢驗了演化式類神經網路技術與智慧資本的有用性，對學術研究與管理實務上的知識累積與傳承，具有正面而好的啟發。

關鍵字(中)

★ 企業危機診斷
★ 遺傳演算法
★ 演化式類神經網路
★ 智慧資本

關鍵字(英)

★ Corporate distress diagnosis
★ Genetic algorithms
★ Evolutionary artificial neural networks
★ Intellectual capital

論文目次

封面
論文提要
目錄
第一章　緒論
第一節　研究背景
第二節　研究動機
第三節　研究目的
第四節　研究問題與重要性
第五節　研究方法與步驟
第六節　研究章節架構
第二章　理論基礎與相關文獻
第一節　企業經營風險理論
第二節　企業經營危機理論
第三節　企業危機的定義
第四節　企業危機診斷模型相關研究
第五節　相關文獻評述
第三章　演化式類神經網路
第一節　類神經網路理論
第二節　倒傳遞演算法
第三節　網路模式選擇與建模的問題
第四節　遺傳演算法
第五節　演化式類神經網路
第四章　智慧資本理論
第一節　知識經濟體系的企業價值：智慧資本之背景
第二節　智慧資本的定義
第三節　智慧資本之理論基礎
第四節　智慧資本分類與衡量
第五節　人力資本的重要性
第六節　智慧資本的整體衡量指標
第七節　本章評論
第五章　理論模型與研究設計
第一節　理論模型的建構
第二節　實證模型設計
第三節　研究設計
第四節　實驗設計
第六章　資料分析與討論
第一節　樣本基本性狀分析
第二節　建模技術續效檢驗
第三節　理論模型之實證分析
第四節　企業危機原因的診斷
第七章　結論與建議
第一節　研究發現
第二節　研究貢獻
第三節　研究限制
第四節　未來研究方向
參考文獻

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指導教授

林子銘(Tzu-Ming Lin)

審核日期

2000-7-18

推文