Sep 2026· Conference Proceedings in Science and Management· Vol 1, pp. 76-81· 5 references
Abstract
The combination of artificial intelligence (AI) and social media analytics provides the means to recognize and predict sustainability-related trends from large volumes of publicly available user data. Its adoption, however, is constrained by concerns about privacy, ethics, and transparency. This bibliometric study analyzed 559 documents from 274 sources, written by 3,414 authors between 2020 and 2025, using RStudio. The annual growth rate was 3.27%, with an average of 71.57 citations per document. Publications in IEEE Access grew significantly, while Scientific Reports and Heliyon grew after 2023. Deep learning, big data, healthcare, privacy, federated learning, and explainable AI were at the center of AI and machine learning research. The findings of this study can help researchers, managers, and policymakers make ethical, data-driven sustainability decisions.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
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It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
