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This paper aims to find out if there is a relationship between access to climate change information and student activism.

Exploratory study focused on the survey of 400 [n = 400] students from 10 universities in Spain from April to May 2022. A questionnaire with 19 questions was divided into blocks of knowledge, awareness, and action and bivariate analysis with a margin of error of ±5% and a confidence level of 95%.

The greater the degree of information received, the greater the activism of university students, who tend to use digital media and social networks to get informed. However, they perceive that the university generates little information and a low number of activities related to climate change. Students demand that universities implement informal, formal, and service-learning environmental education strategies on sustainable consumption.

Given the results of previous studies showing the variable “type of degree” does not show differences at the beginning and end of studies, it has not been considered in this research. Nevertheless, it would be convenient to introduce it in future investigations to confirm if this may have an impact on informational habits.

This paper urges universities to act as sources of environmental education, given the relationship between the information received and the pro-environmental attitudes of students.

The universities are powerful social actors that can shape public and political discourses for eco-social transition.

This research adds the variable access to information in studies on pro-environmental attitudes. Furthermore, this research provides data about student perceptions of the university, government, industry, and NGO climate actions.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.