An empiricalmodel for forecasting technology commercialization performance of software products in universities using genetic programming
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Date
2025-05-20
Journal Title
Journal ISSN
Volume Title
Publisher
Emerald
Abstract
Purpose – This study aims to develop a predictive model using genetic programming (GP) to assess the
technology commercialization performance (TCP) of software products in university settings.
Design/methodology/approach – The prediction model was developed using historical technology
commercialization data from Nigerian university commercialization centers through the application of GP.
Comparative analyses were conducted to assess the results of the GP model against artificial neural network
and support vector machine models. Evaluation metrics such as relative root mean squared error, root mean
absolute error, mean absolute percentage error and coefficient of determination were used to gauge the
accuracy of the models.
Findings – The GP model exhibited a coefficient of determination of 0.9854 and 0.9564 for the training
and testing data, respectively. The comparison of the models using the evaluation metrics demonstrated
that the GP model is superior in its accuracy for forecasting the TCP of software products in university
commercialization centers.
Originality/value – This research has developed a new mathematical equation that depicts the
relationship between TCP and its influential factors. This aspect has not been explored in previous
research. Professionals can use the GP model to strategically allocate resources and improve the
commercialization of software products.