РОЗВИТОК СИСТЕМИ УПРАВЛІННЯ ІННОВАЦІЯМИ У Е-БІЗНЕСІ НА ОСНОВІ РОТОТАБЕЛЬНИХ ПЛАНІВ БОКСА

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It is demonstrated in the article that the application of Box’s rotatable designs for forming and developing an innovation management system in e-business is an effective approach, optimizing processes and enhancing their efficiency. The expansion of the number of factors and levels in the model – particularly by including three levels (low, medium, high) for each factor – enables more accurate assessment of nonlinear effects. This ensures the consideration of both linear and nonlinear interactions, which increases the model's precision. Additionally, it is justified that the inclusion of higher-order interactions, including cubic terms, allows the model to reflect complex interactions among factors. This is particularly crucial for innovation management systems, where factor interactions can have a significant impact on the final outcomes. The use of regularization methods, such as Ridge and Lasso regression, helps to prevent model overfitting and enhances its robustness to data changes, thereby improving its generalization capabilities. It is also proven that incorporating additional factors, such as social and economic influences, accounts for a wider range of aspects affecting innovation levels. This makes the model more comprehensive and accurate, providing a more realistic assessment of the impact of various conditions on innovation. The integration of new factors and regularization methods contributes to building a more precise and reliable model for innovation management in e-business. The article argues that the new model for forming and developing an innovation management system in e-business is superior to the baseline model. It includes more levels for each factor, considers higher-order interactions, and uses regularization methods, ensuring its accuracy and flexibility. This makes the new model more suitable for predicting and managing innovations in e-business compared to the baseline model. Thus, the new model proves more effective for forecasting and managing innovations in e-business, providing higher accuracy, flexibility, and generalizability of results. This is supported by high determination coefficient values and acceptable F-test significance levels. The inclusion of additional factors, consideration of nonlinear effects, and use of regularization methods make the new model better suited for analyzing and managing innovation processes in e-business.