Volume 14 (2022): Edizione 3 (September 2022)

Objective: The aim of this article is to attempt to present theoretical considerations towards the concept of diversity management from the perspective of its location in the discipline of management and quality sciences. As the concept of diversity management lacks a strict demarcation between related disciplines, such as economics, psychology, sociology or even biology and cultural anthropology, it should be noted that the specification of paradigms is not closed, and further paradigms, micro-paradigms or mega-paradigms may emerge over time.

Methodology: the research method adopted in the article is literature analysis and inference.

Findings: The approach presented, which points to the permanent development of alternative paradigms and cognitive perspectives in the discipline of management and quality sciences, is a confirmation that these ‘sciences’ are not ‘impregnated’ against change and are de facto changing.

Value Added: Consideration of the issue of human capital diversity in organisations and its management has been carried out for many years in the literature, which is characterised by a diversity of definitions and perceptions. It is therefore worth presenting, a cross-cutting historical perspective on the phenomenon of human resource diversity in organisations in the discipline of management and quality sciences.

Recommendations: Diversity management should be defined in the broadest possible way, understanding it as the systematic efforts of an organisation to involve the diversity of its human resources in its activities and to treat it as a strategic advantage. Such a conclusion prejudges the need for further research in relation to the concept of diversity management.

Objective: Comprehensive overview of the most current topics, trends and scientific production in the field of behavioral accounting.

Method: A bibliometric approach was applied to analyze data extracted from the Scopus database covering the period 2013–2022. R software and VOS viewer were used to determine the relevant parameters of the studied papers and create scientific maps of collocations.

Findings: An analysis of selected 270 papers has shown that behavioral accounting is a rather scattered area both in terms of publication outputs as well as the conceptual apparatus, including the keywords used by scientists dealing with such issues. This makes it much more challenging to synthesize its output to date and probably slows down the process of crystallizing its scientific identity.

Value Added: It is a diagnosis of the current state of the art within behavioral accounting that can be treated as a continuation of the literature reviews made so far by means of more “manual” methods; however, the first performed with the use of bibliometric tools and devoted exclusively to that topic.

Recommendations: It would benefit the field’s development if researchers parameterized their outputs to facilitate the synthesis of the current state of knowledge within behavioral accounting.

Objective: This study examines sustainability disclosure by 50 British companies from FTSE 100 and compares reporting via traditional sources and on Twitter by indicating whether the content in two various disclosure channels is of substitutionary or complementary nature.

Methodology: A content analysis on more than 20,000 tweets was performed to examine sustainability disclosure practices which were compared with Bloomberg ESG scores for each studied company.

Findings: On the general level of sustainability division into three pillars (Environment, Social and Governance), it can be observed that social media reporting provides complementary information. Whereas, the disclosure of environmental issues via traditional sources was relatively poor, the reporting of environmental information in social media performed best. However, with the division on ESG sub-pillars, the picture is not that clear. Most of the poorly performed ESG sub-pillars in traditional reporting, were also poorly reported in social media.

Value Added: This article is a response to the call for studies on non-financial disclosure via social media, which is strongly highlighted in the recent literature concerning future research. Additionally, a comparative analysis with the reporting by traditional, well-studied channels was performed.

Recommendations: This study offers an understanding of the British companies’ corporate practices that refer to sustainability disclosure by traditional channels and via social media. Hence, it has implications for organizations in the creation and use of communication channels when developing a dialogue with stakeholders on topics regarding sustainability.

Objective: The technology developing before our eyes is entering many areas of life and has an increasing influence on shaping human behavior. Undoubtedly, it can be stated that one such area is trading on stock exchanges and other markets that offer investors the opportunity to allocate their capital. Thanks to widespread access to the Internet and the computing capabilities of computers used in the daily activities of investors, the nature of their working has changed significantly, compared to what we observed even 10–15 years ago. At present, stock exchange orders may be placed in person using various types of brokerage investment accounts, which allow the investor to view real-time quotations which opens up a whole new range of opportunities for investorsIts skillful application during the stock market game can positively influence a player’s investment performance.Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to solve decision-making problems based on large amounts of information. In machine learning, algorithms find patterns and relationships in large data sets and make the best decisions and predictions based on this analysis.

Methodology: The main objective of this paper is to investigate and evaluate the applicability of machine learning for investment decisions in equity markets. The analysis undertaken focuses on so-called day-trading, i.e. investing for very short periods of time, often involving only a single trading session. The hypothesis adopted is that the use of machine learning can contribute to a positive return for a stock market player making short-term investments.

Findings: This paper uses the Azure Microsoft Machine Learning Studio tool to enable machine learning-based calculations. It is a widely available cloud computing platform that provides an investor interested in creating a model and testing it. The calculations were made according to two schemes. The first involves teaching the model by taking 50% of the companies randomly selected from all companies, while the second involves teaching the model by taking 80% of the companies randomly selected from all companies.

Value Added: The results from the study indicate that investors can use machine learning to earn returns that are attractive to them. Depending on the teaching model (50% or 80% companies), daily returns can range from 1.07% to even 4.23%.

Recommendations: The results obtained offer investors the prospect of using the method presented in the article in their capital management strategies, which of course requires them to adapt the techniques used so far to the specifics of machine learning. However, it is necessary to note that the presented method requires that each time the data on which the forecast was made be updated..Further research is needed to determine the impact of the number of companies on the effectiveness of the learning process.