A Research on Simulation Platform for Business Model Innovation: Evolution from Exploration to Ambidextrous Organization

• Main Authors: Pin-Chieh Tasi, 蔡秉玠
• Other Authors: Ming-Hung Chua
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/02936958353379182236

碩士 === 國立中央大學 === 企業管理研究所 === 96 === 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.