Application of Classification to Determine the Level of Awareness of the Foresight Process

Pankratova, N. D.; Savastiyanov, Volodymyr

doi:10.1007/978-3-030-98832-6_7

Cited by 2 publications

(1 citation statement)

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“…Such blended competencies also facilitate the communication between stakeholders, enhancing participation, improving futures literacy and ensuring the quality of processes and results in foresight (Keller and von der Gracht, 2014). To this end, data scientists can contribute significantly by handling data collection, storage and cleaning, while providing accurate interpretations of analyses, in collaboration with practitioners and domain experts (Pankratova and Savastiyanov, 2014;Thu et al, 2022).…”

Section: Effects On Foresight Methodologies and Practicementioning

confidence: 99%

Big data, machine learning and uncertainty in foresight studies

Muraro,

Salles-Filho

2024

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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.

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Section: Effects On Foresight Methodologies and Practicementioning

confidence: 99%