Purpose Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty. Design/methodology/approach The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty. Findings It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice. Originality/value This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

In current research evaluation models, monitoring and impact evaluation are extended beyond peer-reviewed articles to include Public Communication of Science and Technology activities. Through an online survey, we analyzed the perceptions of relevance and degree of application of the altmetric indicators for the PCST of 51 sampled Brazilian federal universities. Perceptions of relevance and application of altmetrics proved to be an outlier in 26 indicators. 66.7% of respondents said they did not know the relevance of altmetrics for the PCST or considered it not applicable to the field. Regarding the perception of relevance, the indicator “Mentions tracked by altmetrics” received high relevance scores (7 and 9) from 21.5% of respondents. The indicator was also the least applied, with only one university (1.9%) using it. In addition, 45% of respondents reported having no intention of applying it, 41.1% intend to apply it in the long term, and 11.7% in the short term.

The aim of the present study was to assess the impacts of the Coffee Social Network (Rede Social do Café, in portuguese) among different categories of users through the proposal of a method based on multidimensional indicators. The CSN shares information about coffee and promotes collaboration and exchange of experiences in the coffee ecosystem. In an online study conducted in 2019 with 366 respondents, we observed an overall measured impact of the CSN of 29.1%, ranging from 25% to 37%, among eight categories of users: Agroindustry, Technical Assistance and Rural Extension (TARE), Commer-cialization, Communication, Education, Research, Agricultural Production, and Other. The most expressive results were found in the Quality, Evolution in Knowledge, and Adoption of Technologies indicators, at 44.4%, 39.4%, and 38.4%, respectively. Such indicators refer to an established pattern in each one of the categories of users and are aligned with the objectives of the CSN and with its most frequent themes. The results found are relevant for the management of the platform, whether as informative content or in relationship with users. Finally, the model of impact assessment proved to be appropriate for the CSN and can be applied in other social networks linked to agribusiness.