Information Management Research Center

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Understanding technological, cultural, and environmental motivators explaining the adoption of citizen science apps for coastal environment monitoring

Publication . Cardoso-Andrade, Mariana; Cruz-Jesus, Frederico; Souza Troncoso, Jesus; Queiroga, Henrique; Gonçalves, Jorge Manuel Santos

Environmental and nature conservation authorities are calling for a collective effort to break or reduce the current cycle of environmental degradation. Much of the response depends on scientific knowledge production based on thematically and geographically comprehensive datasets. Citizen science (CS) is a cost-effective support tool for scientific research that provides means for building large and comprehensive datasets and promoting public awareness and participation. One of the greatest challenges of CS is to engage citizens and retain par-ticipants in the project. Our work addresses this challenge by (1) defining the role that technological, cultural, and environmental dimensions play in the adoption of CS apps for coastal environment monitoring, and (2) providing base knowledge about the profile of the apps' most likely users and the functional features they require to be successful. Collectivists and people who assume a green identity are the most likely users of these apps. Drivers of their use are the promotion of citizen empowerment, habit development, provision of facilitating conditions, and proof of environmental performance.The outcome of this study is a set of guidelines for project managers, app developers, and policymakers for citizens' engagement and retention in CS coastal environment monitoring projects through their apps.

2022-11Journal article

Open access

Large language models powered aspect-based sentiment analysis for enhanced customer insights

Publication . Água, Mariana; António, Nuno; Carrasco, Paulo; RASSAL, CARIMO

In the age of social networks, user-generated content has become vital for organizations in tourism and hospitality. Traditional sentiment analysis methods often struggle to process large volumes of data and capture implicit sentiments. This study examines the potential of Aspect-Based Sentiment Analysis (ABSA) using Large Language Models (LLMs) to enhance sentiment analysis. By employing GPT-4o via ChatGPT, we benchmark three approaches: a fuzzy logic-based method, manual human analysis, and a new ChatGPT-based analysis. We analyze a dataset of 500 all-inclusive hotel reviews, comparing these methods to assess ChatGPT's effectiveness in identifying nuanced language and handling subjectivity. The findings reveal a high similarity between ChatGPT and human analysis, showcasing ChatGPT's ability to interpret complex sentiments and automate sentiment classification tasks. This study highlights the potential of LLMs in transforming customer feedback analysis, providing deeper insights, and improving responsiveness in the hospitality industry. These results contribute to academia by presenting a framework for using LLMs in ABSA and guiding future applications and development.

2025-01-01Journal article

Open access