Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management

Armenia, Stefano; Franco, Eduardo; Iandolo, Francesca; Maielli, Giuliano; Vito, Pietro

doi:10.1016/j.techfore.2023.123131

Technological Forecasting and Social Change

2024

DOI: 10.1016/j.techfore.2023.123131

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Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management

Stefano Armenia,

Eduardo Franco,

Francesca Iandolo

et al.

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Cited by 8 publications

References 206 publications

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A topic modeling‐based bibliometric exploration of automatic summarization research

Chen,

Xie,

Tao

et al. 2024

WIREs Data Min & Knowl

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The surge in text data has driven extensive research into developing diverse automatic summarization approaches to effectively handle vast textual information. There are several reviews on this topic, yet no large‐scale analysis based on quantitative approaches has been conducted. To provide a comprehensive overview of the field, this study conducted a bibliometric analysis of 3108 papers published from 2010 to 2022, focusing on automatic summarization research regarding topics and trends, top sources, countries/regions, institutions, researchers, and scientific collaborations. We have identified the following trends. First, the number of papers has experienced 65% growth, with the majority being published in computer science conferences. Second, Asian countries and institutions, notably China and India, actively engage in this field and demonstrate a strong inclination toward inter‐regional international collaboration, contributing to more than 24% and 20% of the output, respectively. Third, researchers show a high level of interest in multihead and attention mechanisms, graph‐based semantic analysis, and topic modeling and clustering techniques, with each topic having a prevalence of over 10%. Finally, scholars have been increasingly interested in self‐supervised and zero/few‐shot learning, multihead and attention mechanisms, and temporal analysis and event detection. This study is valuable when it comes to enhancing scholars' and practitioners' understanding of the current hotspots and future directions in automatic summarization.This article is categorized under: Algorithmic Development > Text Mining

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A topic modeling‐based bibliometric exploration of automatic summarization research

Chen,

Xie,

Tao

et al. 2024

WIREs Data Min & Knowl

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Smart cities, smart systems: A comprehensive review of system dynamics model applications in urban studies in the big data era

Fabolude,

Knoble,

et al. 2024

Geography and Sustainability

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Bridging Complexity: Business Incubators in the Preliminary Lens of System Dynamics Simulation

Almeida,

Afonso,

Soares

et al. 2024

Procedia CIRP

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Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management

Cited by 8 publications

References 206 publications

A topic modeling‐based bibliometric exploration of automatic summarization research

A topic modeling‐based bibliometric exploration of automatic summarization research

Smart cities, smart systems: A comprehensive review of system dynamics model applications in urban studies in the big data era

Bridging Complexity: Business Incubators in the Preliminary Lens of System Dynamics Simulation

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