Advanced Quantitative Techniques in Entrepreneurship Research

A special issue of Administrative Sciences (ISSN 2076-3387).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2777

Special Issue Editors


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Guest Editor
Department of Business Administration, University of Patras, University Campus, 26504 Rio Achaia, Greece
Interests: computational statistics; digital finance; extreme value theory; financial econometrics; quantitative finance; risk management; volatility and times series analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Accounting and Finance, Hellenic Mediterranean University, Heraklion, Crete, Greece
Interests: financial economics; financial econometrics; risk management; banking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Administrative Sciences invites submissions to a Special Issue on the topic of “Advanced Quantitative Techniques in Entrepreneurship Research”. This Special Issue will try to explore essential aspects of entrepreneurial potential. Specific aspects include, but are not limited to, Artificial Intelligence (AI), Machine Learning (ML), Internet-of-Things (IoT), Data Mining, statistical modelling and model selection, approaches for timeseries modelling and forecasting, classification and statistical learning. It aims to bring together novel design methodologies and their application on various perspectives of entrepreneurial activities, processes and outcomes. Particularly, it will highlight empirical studies that investigate and develop technology driven models to harness and propose solutions for different entrepreneurial challenges. The Special Issue invites contributions from a wide range of disciplines including, but not limited to, statistics, computer science, business, economics and finance.

This Special Issue is open to new methodological developments and state-of-the-art quantitative techniques with applications on entrepreneurship. It seeks to push the boundaries of entrepreneurship research by welcoming original unpublished work not being considered for publication elsewhere. Manuscripts can be submitted until the deadline. Additional submission guidelines to be followed are provided on the journal’s homepage: https://www.mdpi.com/journal/admsci.

Dr. Konstantinos Gkillas
Prof. Dr. Christos Floros
Guest Editors

Manuscript Submission Information

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Keywords

  • entrepreneurship
  • machine learning
  • advance statistical methods

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Published Papers (1 paper)

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Research

30 pages, 1037 KiB  
Article
The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises
by Dominika Gajdosikova, Katarina Valaskova and George Lazaroiu
Adm. Sci. 2024, 14(2), 26; https://doi.org/10.3390/admsci14020026 - 30 Jan 2024
Cited by 4 | Viewed by 2116
Abstract
The processing and transformation of natural resources into completed and semi-finished products is the primary function of industry in each nation’s economy. There is no denying the significance of industry and sectoral classification of the economy, but the slow development and extension of [...] Read more.
The processing and transformation of natural resources into completed and semi-finished products is the primary function of industry in each nation’s economy. There is no denying the significance of industry and sectoral classification of the economy, but the slow development and extension of one industry could have resulted in the advancement of other sectors that are now a part of contemporary communities. Since there are statistically significant differences between various industries, numerous authors are currently investigating the impact of the industry on the financial structure of firms, revealing the industry as a crucial determinant of corporate indebtedness. Thus, the main aim of this study is to determine the debt level of a sample of 4237 enterprises operating in the market in the period of 2018–2021 from various sectors using eight debt indicators, as well as to identify relationships between them, which may help to reveal sectors with homogeneous patterns of indebtedness (using the cluster analysis) and thus understand which sectors are the most stable and independent. The Kruskal–Wallis test is then used to determine if there are statistically significant differences between the calculated ratios related to the economic sector. Based on the results, it can be concluded that the choice of financial structure is significantly influenced by the industry. Financial performance and indebtedness indicators are quantitative statistics used to assess, monitor, and forecast company or sectoral financial health. They act as instruments for business insiders and outsiders to assess a company’s performance, particularly in comparison to competitors, and to pinpoint its strengths and weaknesses, making the outputs of this study important for all types of stakeholders. Full article
(This article belongs to the Special Issue Advanced Quantitative Techniques in Entrepreneurship Research)
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