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The purpose of this work is twofold. First, looks to identify the main homogenous groups of companies after environmental, social, economic and governance (ESEG) disclosures, non-financial statement and earnings per share (EPS), and second investigates the connection between variables.

Using financial and non-financial information from annual reports of private listed companies, the authors performed two-step cluster analysis (TSCA) in the first stage of the research, followed by parametric, nonparametric correlation analysis, as well as regression analysis based on panel data, in the second stage.

Results of TSCA revealed a cluster of companies with good financial and non-financial outcomes and a cluster of companies with poor performance. The performance dynamics showed a slight improvement during the period for few companies and composition analysis of clusters by industries through Kruskal–Wallis test highlighted differences between clusters, only for 2017. The main findings confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and financial performance (FP), valid for the entire period. Also, the results showed a direct link of low intensity to average, but statistically significant between the non-financial statement and EPS, valid only for 2017 and 2018.

The results indicate mixed findings which invites further in-depth research. Limits of the study can be found in selected indicators and the short period of time analyzed. However, the practical implications are worth considering from the perspective of finding new managerial tools that can better shape the relationship between ESEG disclosures and FP.

ESEG Dindx can be an instrument for managers that can optimize the link between the FP of companies and its sustainable development.

ESEG Dindx measures the disclosure degree of ESEG information by the companies listed on Bucharest Stock Exchange (BSE). The main findings of the work confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and FP, valid for the entire period.

This study adds value to the existing literature by the proposed research framework, design of ESEG Dindx and the way correlations between variables were investigated.

Purpose/Objective: The study explores the moderating effect of ESG performance on the connection between information relevance, accurate representation, and financial performance.

Methodology: The moderating effects were examined based on ESG data available in the Eikon-Refinitiv platform for 2017–2021. A total of 28 regression models were estimated using the Panel Generalised Least Squares method, and four specifications of each model were developed for the ESG score and its components. Financial performance was measured by ROE, ROA, EPS, NetOCF, EBIDTA, and CR indicators.

Findings: Both the ESG score and its three components moderate in a statistically significant manner the relationship between the relevance of financial information and financial performance. The moderation effect is partially confirmed in the case of ROA, ROE, EBITDA, and EPS indicators. Results highlighted that both the ESG score and its components moderate in a statistically significant manner the relationship between the accurate representation of information and financial performance in the case of EPS, Cash_R, and CR indicators.

Implications: The ESG component variables and performance score directly influence the relevance and inversely and significantly influence the accurate representation of financial information. New business performance optimisation models can be designed based on the main findings.

Limitations: The small number of companies in the sample and limits on information available on ESG performance.

Future Research: Expanding the number of companies and variables in the statistical models. Various mathematical models for estimating optimal performance can be tested depending on the size of the data set.

This article, the first empirical study of its kind, presents findings from a larger qualitative study of principal mistreatment of teachers. A grounded theory method was used to study a sample of 50 US teachers who were subjected to long‐term mistreatment from school principals. The authors discuss descriptive, conceptual, and theoretical findings about principals’ actions that teachers define as mistreatment. In addition, the inductively derived model briefly looks at the harmful effects of principal mistreatment and abuse on teachers, psychologically/emotionally and physically/physiologically. Implications of study findings are discussed for administrator and teacher preparation, for school district offices, and for further research.

This chapter explains the research design. An interpretive methodology was considered most suitable for the study. Informed by an institutional framework, the interpretive methodology was selected for this monograph for its strengths of focusing on the research context, interactive processes, and meanings that are not measurable by quantitative approach. The interpretive methodology is also consistent with the ontological and epistemological positions of the researchers. Data were collected from interviewing four groups of key persons and a document survey. The data triangulation and multiple perspectives helped increase the reliability and validity of the study. Also, conducting data collection in a natural setting produced a rich data source. This enabled the provision of an enhanced understanding of the operation and effectiveness of corporate governance and financial reporting practice in a real setting. In addition, the systematic set of data analysis procedures helped improve research rigor and develop conceptual and theoretical understanding of issues of interest.

This paper describes the methodology of a PhD thesis that was a study of the regulatory practices in the private health care sector of Bangladesh. The paper begins by situating the methodology in the nature, context and significance of the study. As the study involved a policy analysis and evaluation these are defined and described. The paper concentrates on the research design and methodology, which involved the use of qualitative methods. The sampling is described and the methods used included in‐depth interviews, the taking of field notes based on observations and document analysis. Attention is also given to ethical issues. The problems that emerged and the limitations of the study design and methodology are also discussed.

The use of data for instructional improvement is prevalent in today’s educational landscape, yet policies calling for data use may result in significant variation at the school level. The purpose of this paper is to focus on tools and routines as mechanisms of principal influence on data-use professional learning communities (PLCs).

Data were collected through a comparative case study of two low-income, low-performing schools in one district. The data set included interview and focus group transcripts, observation field notes and documents, and was iteratively coded.

The two principals in the study employed tools and routines differently to influence ways that teachers interacted with data in their PLCs. Teachers who were given leeway to co-construct data-use tools found them to be more beneficial to their work. Findings also suggest that teachers’ data use may benefit from more flexibility in their day-to-day PLC routines.

Closer examination of how tools are designed and time is spent in data-use PLCs may help the authors further understand the influence of the principal’s role.

Previous research has demonstrated that data use can improve teacher instruction, yet the varied implementation of data-use PLCs in this district illustrates that not all students have an equal opportunity to learn from teachers who meaningfully engage with data.

Biologically inspired techniques like cellular automata (CA) are gaining nowadays attention of designers. This is because they are effective, do not require gradient information, and one can easily combine this type of algorithm with any finite element structural analysis code. The purpose of this paper is to develop a CA algorithm based on novel local rules oriented at solving compliance-based topology optimization problems.

The design domain is divided into lattice of cells, states of which are updated synchronously. The proposed rules include information coming from an individual cell and from its neighborhood, and by introducing weighting parameters allow to control and modify topology generation process.

The performance of the developed algorithm is very satisfactory, and a comparison with results of other authors, obtained with the use of various optimization techniques, shows efficiency of the present topology generation process. The results found within approach of this paper are in a good agreement with the ones already reported, both for optimal topologies and values of minimal compliance, which in some cases are found even improved.

The algorithm presented in the paper is quite general what allows its easy application to engineering design problems. Moreover, the local update rules are simple, so they can be easily implemented into professional FEM analysis codes, as an efficient add-on module for topology optimization.

The main advantage of the developed algorithm is that it is a fast convergent technique and usually requires far less iterations as to achieve the solution, when compared to other approaches. What is also important does not require any additional density filtering. It also overcomes some drawbacks of traditional approaches so that changing mesh density does not influence resulting topologies and solutions are free from checkerboard effect.