Development of an innovation management strategy for tourism organizations using neural networks

The innovative activities management in tourism companies in the context of accelerated digital transformation has been studied. The purpose of the study is to develop and empirically validate a strategic management decision support model based on a hybrid architecture of artificial neural networks. The model is designed to predict the effectiveness of innovation initiatives and make recommendations on choosing the optimal type of innovation (product, process, or marketing).

The hypothesis of the study is that a joint analysis of the external environment through the processing of unstructured text data and an assessment of a company’s internal innovation potential will improve the accuracy and adaptability of strategic management in tourism.

The methodological base of the study includes the collection and pre-processing of multi-source data such as financial and operational indicators of travel companies, user reviews, as well as digital maturity indices. A pre-trained BERT language model, bidirectional LSTM networks, and a multilayer perceptron have been used to build the model. Training and quality assessment have been conducted using k-fold cross-validation and the SHAP method for interpreting the results. The experiment has shown the accuracy of predicting the success of innovation at the level of 86.4%. During the pilot implementation, ROI from innovative projects increased by 12–27% in 10 companies.

The scientific novelty of the research lies in creating an integrated neural network model that combines predictive and prescriptive functions in the context of strategic innovation management in tourism.

The study results confirm that the use of neural network technologies increases the validity and flexibility of forming an innovation strategy. The model can be implemented in the practice of strategic planning of hotels, tour operators, online aggregators, as well as used by government agencies in the development of industry innovation policy.

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