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首页> 外文期刊>Journal of Entrepreneurship & Organization Management >Developing a Client Performance Evaluation Model using Machine Learning Methods for a Three-Stage Technology Incubation Process
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Developing a Client Performance Evaluation Model using Machine Learning Methods for a Three-Stage Technology Incubation Process

机译：使用机器学习方法为三阶段技术孵化过程开发客户绩效评估模型

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摘要

Technology incubators, where new early-stage ventures accommodate in a supportive environment, are younger than 15 years of age in Iran. Nevertheless, it is necessary to localize the technology incubator models based on such parameters as culture, human resources, level of technology, and education system so as to meet an appropriate effectiveness. To achieve this goal, the present paper firstly introduces a three-stage incubation model considering special characteristics of the studied country. In this proposed model, the pre-incubation stage is the same as other currently used models but the incubation stage breaks down into two new stages namely technology incubation and technology development. The new model enhances market concentration and encourages incubator clients to finalize their products/services. This model has been successfully implemented in Kerman Technology Incubator and our experimental studies and evidences show the effectiveness of the proposed approach in improving the performance of the incubator. At the second phase, a machine learning evaluation model is developed with an aim to measure the incubator’s client performance. This model utilizes the advantages of classification algorithms for mapping the business success factors into quality of client level. Hence, different classification methods are applied and their performances have been compared together. Results show the efficiency of the developed model in terms of accuracy.
机译：在伊朗，年龄不超过15岁的技术孵化器可容纳新的早期风险投资机构。但是，有必要根据文化，人力资源，技术水平和教育系统等参数对技术孵化器模型进行本地化，以达到适当的效果。为了实现这一目标，本文首先考虑被研究国家的特点，引入了一个三阶段的孵化模型。在该模型中，预孵化阶段与其他当前使用的模型相同，但是孵化阶段分为两个新阶段，即技术孵化和技术开发。新模式提高了市场集中度，并鼓励孵化器客户最终确定其产品/服务。该模型已在Kerman Technology孵化器中成功实施，我们的实验研究和证据表明，该方法在提高孵化器性能方面是有效的。在第二阶段，开发了一种机器学习评估模型，旨在评估孵化器的客户表现。该模型利用分类算法的优势将业务成功因素映射到客户级别的质量中。因此，应用了不同的分类方法并将它们的性能进行了比较。结果显示了所开发模型的准确性。

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