根據IBM近幾年的調查報告，各公司的CEO對經營模式的重視程度越來越高，而在報告中也險示，經營模式有經過改變的公司績效也大於沒反應的公司，因此實務上對經營模式改善的興趣漸漸提高，而學術上如何提高與實務上的溝通，將是一大挑戰。另外，經營模式的活動越多，活動間的影響關係越複雜，要瞭解其經營模式的內涵越不容易，而其經營模式內的活動也不易操弄。故本研究嘗試以因果脈絡圖來分析經營模式，以更簡化和視覺化的方式來呈現經營模式，也進一步操弄這些經營活動元件，並使用Nvivo分析軟體來輔助紮根分析過程，同時也利用Pajek軟體幫助本研究繪出經營元件因果脈絡圖，最後使用BayesiaLab軟體協助本研究做有關經營模式間的分析。 為了瞭解探勘型企業創新演進成併用型企業各種可能的狀況，利用本研究所建立的16分類經營模式，並建立一個具紮根基礎的模擬試驗平台，藉由經營模式內活動元件關係的操弄，探討兩經營模式間的移動，其經營活動會有怎樣的聯繫關係，還有利用經營活動元件設定不同情境，觀察其對標的元件的影響，而主要的觀察經營活動元件為以伺服骨幹、樞軸及兩者兼具的併用型骨幹元件為主。 As a company engages in more and more business activities, relations amongst these activities tend to get increasingly complicated. In order to explore the connotations underlying these activities, the research aims to construct an experimental platform to manipulate causal maps and simulate Bayesian Networks built upon elements representing corresponding business activities. This research has proposed a 16-grid typology which serves possible directions for innovative improvement of the target company. The research has also constructed core element frameworks suited for exploitative, exploratory and ambidextrous organizations respectively. Through the platform mentioned above, the research hopes to explore the ways in which an exploitative organization can enhance her existing exploitative activities and learn new exploratory activities for higher profit margin and better market opportunities. As well as to identify the business elements which pose the greatest effect on profit margin and market opportunities. The research adopts a qualitative and quantitative approach to gain better understanding of the casual relationships among these business elements. The qualitative part of this research includes grounded coding of the interview transcripts, construction of causal maps using elements extracted through grounded coding, and finally structural analysis of the casual maps. The software utilized are Nvivo, Pajek and Ucinet. The quantitative part of this research includes construction of Bayeisan Networks from the causal maps, and simulation of the degree of causal influence among the business elements. As well as discovering the dominant skeleton of the business model. The software utilized is BayesiaLab.
