The primary objective of this study is to reveal macro-level knowledge to aid the optimization, evaluation, and strategic planning of technological innovation abandonment. This research uses an exploratory data analysis (EDA) approach to extract directional and associative patterns (macro-level knowledge) to assess technological innovation abandonment optimization. Deterministic and stochastic simulations are employed to reveal the impact of three factors on abandonment optimization, namely, a technological innovation’s diffusion rate, a technological innovation’s probability of achieving a given diffusion rate, and the point of abandonment. The patterns and insights revealed through the graphical examination of the simulation provide associative and directional knowledge to assess the abandonment optimization of technological innovation. These revealed patterns and insights enable decision-makers to develop an abandonment assessment framework for optimizing, evaluating, and proactively planning abandonment at the macro level.

中文翻译：

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优化技术创新的放弃

这项研究的主要目的是揭示宏观知识，以帮助技术创新放弃的优化，评估和战略规划。这项研究使用探索性数据分析（EDA）方法提取方向和关联模式（宏观知识），以评估技术创新放弃的优化。使用确定性和随机模拟来揭示三个因素对放弃优化的影响，即技术创新的扩散率，技术创新达到给定扩散率的概率以及放弃的点。通过模拟的图形检查揭示的模式和见解提供了关联和方向性知识，以评估技术创新的放弃优化。