Purpose: This paper aims to analyse the factors that influence the satisfaction and intention of continuity of use, of teachers and students, regarding using Moodle in undergraduate courses in one Campus at the Federal Institute of Rondônia in Brazil. The starting point was an integration of DeLone and McLean's Information Systems Success Model (ISSM) with Davis' Technology Acceptance Model (TAM).Design/methodology/approach: A quantitative research approach was adopted. After the definition of the hypotheses, data were collected through self-administered questionnaires. The questionnaires were designed to measure the five constructs: Quality of Information (QI), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), User Satisfaction (US) and Behavioural Intention to use (BI) that make up the conceptual model of the study. The data were analysed based on 144 valid questionnaires. The technique of maximum likelihood estimation was adopted in the data analysis through structural equation modelling (SEM).Findings: The results confirmed six of the nine hypothesised relationships. QI positively impacts PEOU and US. PEOU positively impacts PU, which in turn positively impacts US and BI. Similarly, US positively impacts Moodle's BI. It was also evidenced that PU is the strongest predictor of US.Practical implications: These results can help educational institutions, managers, administrators and designers of e-learning systems to develop strategies to increase Moodle's user satisfaction.Originality/value: This study provides insights into the perception of students and teachers regarding the use of Moodle. A model that integrates constructs from two models widely used in research related to e-learning (TAM and ISSM) was used in a developing country context. This is important, given cultural differences and social idiosyncrasies in different contexts, particularly in an educational institution in the Amazonia region in northern Brazil.

The pandemic of the coronavirus disease 2019 (COVID-19), has gained extensive coverage in public media and global news, generated international and national communication campaigns to educate the communities worldwide and raised the attention of everyone. The coronavirus has caused viral pneumonia in tens of thousands of people around the world, and the COVID-19 outbreak changed most countries’ routines and concerns and transformed social behaviour. This study explores the potential use of Google Trends (GT) in monitoring interest in the COVID-19 outbreak and, specifically, in personal protective equipment and hand hygiene, since these have been promoted by official health care bodies as two of the most protective measures. GT was chosen as a source of reverse engineering data, given the interest in the topic and the novelty of the research. Current data on COVID-19 are retrieved from GT using keywords in two languages—Portuguese and Polish. The geographical settings for GT are two countries: Poland and Portugal. The period under analysis is 20 January 2020, when the first cases outside China were known, to 15 June 2020. The results show that there is a correlation between the spread of COVID-19 and the search for personal protective equipment and hand hygiene and that GT can help, to a certain extent, understand people’s concerns, behaviour and reactions to sanitary problems and protection recommendations.

This paper provides an overview of the empirical research on cultural intelligence, with a special focus on discussing key practical benefits to individuals, teams and organizations. The importance of cultural intelligence training is also highlighted.

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.

Digital Transformation (DT) has become an important issue for organisations. It is proven that DT fuels Digital Innovation in organisations. It is well-known that technologies and practices such as distributed ledger technologies, open source, analytics, big data, and artificial intelligence (AI) enhance DT. Among those technologies, AI provides tools to support decision-making and automatically decide. Cooperatives are organisations with a mutualistic scope and are characterised by having participatory cooperative governance due to the principle of democratic control by the members. In a context where DT is here to stay, where the dematerialisation of processes can bring significant advantages to any organisation, this article presents a critical reflection on the dangers of using AI technologies in cooperatives. We base this reflection on the Portuguese cooperative code. We emphasise that this code is not very different from the ones of other countries worldwide as they are all based on the Statement of Cooperative Identity defined by the International Cooperative Alliance. We understand that we cannot stop the entry of AI technologies into the cooperatives. Therefore, we present a framework for using AI technologies in cooperatives to avoid damaging the principles and values of this type of organisations.