Mapping Risks Faced by Startup Investors: An Approach Based on the Apriori Algorithm

Silva Júnior, Claudio Roberto; Siluk, Julio Cezar Mairesse; Neuenfeldt-Júnior, Alvaro Luis; Francescatto, Matheus Binotto; Michelin, Cláudia de Freitas

doi:10.3390/risks11100177

Risks

2023

DOI: 10.3390/risks11100177

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Mapping Risks Faced by Startup Investors: An Approach Based on the Apriori Algorithm

Claudio Roberto Silva Júnior,

Julio Cezar Mairesse Siluk,

Alvaro Luis Neuenfeldt-Júnior

et al.

Abstract: This article maps and verifies the dependence relation between risks faced by startup investors. Thus, a systematic review of 33 articles and a meta-analysis using the Apriori algorithm were used. We mapped 14 investment risks faced by startup investors, classifying them into four dimensions: external, internal, human, and capital. Furthermore, by using the Apriori algorithm, dependency relations between nine investment risks were observed. This research fills a gap related to the non-structuring of a holistic… Show more

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2024

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Cited by 2 publications

References 75 publications

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Overview of Startups Developing Artificial Intelligence for the Energy Sector

Mohammadi Lanbaran,

Naujokaitis,

Kairaitis

et al. 2024

Applied Sciences

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The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

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Overview of Startups Developing Artificial Intelligence for the Energy Sector

Mohammadi Lanbaran,

Naujokaitis,

Kairaitis

et al. 2024

Applied Sciences

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Decision-Making for Efficiency and Innovation in the Rice Production Chain

Lhamby,

Neuenfeldt Júnior,

Siluk

et al. 2024

REI

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The main objective of this article is to conduct a systematic review within the rice sector's agri-business productive chain, focusing on aspects related to the decision-making process, innovation environments, competitiveness, and methodologies for measuring performance and differentiation. The article emphasizes the importance of the agribusiness and the need for a systemic approach to addressing challenges encountered within the production chain. It highlights that pursuing competitiveness can lead to reducing production costs and adding value, and gaining competitive advantages through differentiation strategies. Within the context of the rice sector, the significance of rice is highlighted as one of the most widely cultivated cereals globally, with significant social, economic, and environmental impacts. However, fluctuations in global rice prices can result in undesirable effects. Therefore, new strategies are needed to add value to products and expand consumption beyond the conventional markets.Given this context, the article contributes to the literature by identifying the decision-making processes applied in these agroindustries, as well as addressing aspects related to innovation environments, competitiveness, and methodologies for measuring performance and differentiation.

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Mapping Risks Faced by Startup Investors: An Approach Based on the Apriori Algorithm

Cited by 2 publications

References 75 publications

Overview of Startups Developing Artificial Intelligence for the Energy Sector

Overview of Startups Developing Artificial Intelligence for the Energy Sector

Decision-Making for Efficiency and Innovation in the Rice Production Chain

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