當經營模式的活動越多，活動間的影響關係越複雜，造成經營模式的內涵越不容易了解，而其經營模式內的活動也不易運作。故本研究嘗試以紮根因果脈絡圖來分析經營模式內部的活動與資源，並以貝氏機率理論建立經營模式的創新模擬平台，提供進一步操弄這些經營模式元件的機會。而本研究透過建立16分類經營模式，提供企業經營模式創新改善的途徑建議。 此外，本研究也提出開採、探勘以及併用三種經營模式核心骨幹的架構，形成經營模式創新改善的主軸。透過上述的平台以及改善方式，本研究希望藉由經營模式內開採以及併用相關活動元件的操弄，探討企業如何基於既有的開採型特徵，學習探勘新資源的能力，以獲得更高的企業實質收益與市場機會，同時也觀察並找出對企業實質收益與市場機會造成重大影響的關鍵經營模式元件。 本研究為了對經營活動的因果脈絡有更好的理解，同時採用質性與量性的研究方法進行分析。在質性的部份，本研究透過對訪談個案文本經營模式因果脈絡的紮根編碼，並配合Nvivo、Pajek、Ucinet等分析軟體，將已編碼的元件轉為經營模式的因果脈絡圖。而在量性的部份，本研究將因果脈絡圖導入貝氏網路，透過BayesiaLab軟體的協助，以機率理論模擬經營模式元件間影響程度的高低，並找出經營模式中主要的脈絡。 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.
