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Financial Sustainability of Companies and Organizations under Globalization
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Financial Sustainability of Companies and Organizations under Globalization

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 18782

Special Issue Editor

Dr. Dimitrios Dimitriou

E-Mail Website
Guest Editor
Department of Business Administration, Athens University of Economics and Bussiness, Athens, Greece
Interests: econometrics; financial economics; macroeconomic forecasting

Special Issue Information

Dear Colleagues,

The scope of the Special Issue ”Financial Sustainability of Companies and Organizations under Globalization” focuses on the financial part of the triple-bottom-line (TBL) model, as well as the relationships that develop with the other two parts. In particular, the current issue aims to identify the determinants of financial sustainability its implications and interactions within the environmental and social sector. Furthermore, possible contagion effects among financial, enviromental and social sustainability may be examined thoroughly.

Focusing on corporate sustainability, it explores why companies and organizations engage sustainability and what modern growth model they should follow. In the macro environment, the contribution of each country is examined, as well as the interaction between the countries and the contribution of global businesses. Overall, all topics related to social and financial sustainability are welcome.

Papers selected for this Special Issue were subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments and applications.

Dr. Dimitrios Dimitriou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • global crisis
  • financial sustainability
  • corporate sustainability
  • social sustainability
  • contagion effects

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Published Papers (3 papers)

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Research

18 pages, 5148 KiB  
Article
Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
by Der-Jang Chi and Chien-Chou Chu
Sustainability 2021, 13(21), 11631; https://doi.org/10.3390/su132111631 - 21 Oct 2021
Cited by 8 | Viewed by 2938
Abstract
“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of [...] Read more.
“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy. Full article
(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)
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20 pages, 2402 KiB  
Article
Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry
by Chyan-Long Jan
Sustainability 2021, 13(17), 9879; https://doi.org/10.3390/su13179879 - 2 Sep 2021
Cited by 33 | Viewed by 8661
Abstract
Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep [...] Read more.
Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator. Full article
(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)
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17 pages, 462 KiB  
Article
Hofstede’s Cultural Dimensions as a Moderator of the Relationship between Ambidextrous Learning and Corporate Sustainability in Born Global Firms
by Diana Escandon-Barbosa, Jairo Salas-Paramo and Josep Rialp-Criado
Sustainability 2021, 13(13), 7344; https://doi.org/10.3390/su13137344 - 30 Jun 2021
Cited by 5 | Viewed by 6132
Abstract
This research analyzes the moderation effects of Hofstede’s Cultural Dimensions (Power Distance, Uncertainty Avoidance and Indulgence) in the relationship between Ambidextrous learning and corporate sustainability in born global firms. The data were collected from exporting firms characterized by beginning international operations in the [...] Read more.
This research analyzes the moderation effects of Hofstede’s Cultural Dimensions (Power Distance, Uncertainty Avoidance and Indulgence) in the relationship between Ambidextrous learning and corporate sustainability in born global firms. The data were collected from exporting firms characterized by beginning international operations in the first three years and were thus classified as Born Global. A panel Dynamic Structural Equation Model (DSEM) was used to test the research hypothesis. One of the methodological contributions is the exploration of dynamic social behaviors that are difficult to study, specifically over time. Here, DSEM becomes in a data analysis technique that allows us to analyze this type of phenomena. The research results show that the relationship between Ambidextrous learning (AL) and Corporate Sustainability (CS) is positive in the short- and long-term. The cultural dimension’s Power Distance and Uncertainty Avoidance moderates the relation between (AL) and (CS) and this dimension can predict their inertia. However, while Uncertainty Avoidance has a moderating effect, it does not predict future behaviors. Published literature on the Born Global company. that includes the moderation of Hofstede’s dimensions (Power distance, Avoidance of uncertainty, and Indulgence) from a company perspective that study the relationship between Ambidextrous Learning and Corporate Sustainability is scarce. Full article
(This article belongs to the Special Issue Financial Sustainability of Companies and Organizations under Globalization)
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