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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.

Developing a Client Performance Evaluation Model using Machine Learning Methods for a Three-Stage Technology Incubation Process