Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

The research defined the objective of building and validating instruments to assess the level of maturity of digital transformation in higher education institutions (HEIs). It was based on a theoretical-methodological model that specified eight dimensions of analysis. The methodology included 7 phases: contextualization of the instruments, initial proposal, iterative review and improvement, content validation, pilot test, reliability analysis and exploratory factor analysis (EFA). The results of the pilot test, conducted with more than 100 participants, showed adequate levels of reliability and validity. The EFA found an adjusted factorial structure of the instruments, with approximate factors of 59.7% for students, 64.8% for teachers, 72.79% for administrators and 81.9% for managers, being considered acceptable and suggesting that they effectively capture the dimensions of analysis. In addition, it was observed that some items originally assigned to specific dimensions were regrouped into conceptually related factors, suggesting the need for adjustments in the initial proposals. Based on the results obtained, these instruments have the potential to be effective tools for identifying digital maturity in HEIs, providing a comprehensive diagnosis to guide strategic decisions and technology investment planning for HEIs. Future studies could be complemented with the perception of graduates and the increase of the sample.