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This study aims to attempt to evaluate and establish the relationship between gender diversity (GD) on the board and corporate sustainability performance.

A sample of 212 non-financial companies listed on the National Stock Exchange has been considered for a period of 2013–2014 to 2018–2019. For the purpose of the analysis, this study has conducted the static panel data model analysis and also some diagnostics tests to arrive at robust results.

This study, from its analysis, interprets that GD or the proportion of women directors in the company plays a significant role in the decisions related to the sustainability performance of the company. Alongside GD, the profitability of the company, measured in terms of Tobin’s Q, and firm size are also seen to have a positive impact on the sustainability performance of the company.

This study from its findings contributes to the existing works of literature by highlighting the impact of GD on the sustainability performance of the firm. This study thus recommends the recruitment of an ample number of females in the top-notch positions of the board to create a gender-diverse management team to reap the benefits of leadership styles of both genders.

Very few studies have been conducted on the dynamics of women’s directorship, especially in an emerging economy like India. This study thus tries to fill this important gap in the literature by examining the relationship between board GD and sustainability performance of Indian firms.

Taking cues from the fact that there remains a dearth in the establishment of theoretical and empirical relationship between executive compensation and corporate social responsibility (CSR) performance of the firms, this study attempts to explore the non-linear relationship between the said variables.

The study utilizes a strongly balanced panel data set of 179 non-financial National Stock Exchange (NSE) 500 listed firms for the study period of 2015–2020. The study further employs both static as well as Arellano-Bond dynamic panel model under generalized method of moments (GMM) framework to establish the relationship between executive compensation and CSR performance of the sampled firms.

The study acknowledges an inverted U-shaped relationship between executive compensation and environmental, social and governance (ESG) score of the firms. According to the robust estimator, an increase in the level of executive compensation is said to affect CSR performance positively until it surpasses a threshold level of 18.7 percent.

One of the major takeaways that the study provides for the corporate policymakers is that the level of compensation can only motivate the executives to take up socially responsible work up to a certain level surpassing which the executives becomes resistant towards any benefits provided by the CSR performance and get inclined towards economical performances of the firm. At the later stage, the economical expansionary investment benefits overweigh the personal career benefit gained by the executives from the CSR performances of the firm.

The nonlinearity relationship between executive compensation and CSR performance and the threshold level providing the two-fold effect of compensation on the CSR performance of the firms attempted by this study is a rare attempt in an emerging economy like India.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

The study visualizes the link between environment accounting & triple bottom line, quantitative environmental reporting & standard method, voluntary environmental disclosure & legal requirement, size of company & volume of environmental disclosure, material flow analysis & life cycle assessment to achieve sustainable development in Bangladeshi corporation. Therefore, the purpose of the study is to investigate the role of these factors to achieve sustainable development in Bangladeshi corporation. To investigate the role of these factors, ten factors that significantly contribute to achieve sustainable development were determined. A set of closed-minded questionnaire was developed on the basis of these factors to collect the data from employees & employers. Questionnaire was administered by using statistical tools such as matrix, cross tabulation & Paired Samples Tests as a data collection tool and analyses. Research finding shows that sustainability of corporation was associated with the performance of economic, social, and environment. Other factors like quantitative environmental reporting, standard method, voluntary environmental disclosure, legal requirement, size of the company, volume of environmental disclosure, material flow analysis & life cycle assessment were found that they worked as a complement to enhance the performance of economic, social, and environment to achieve sustainable development in Bangladeshi corporation.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

This paper investigates the global and local interest in Internet information about cryptocurrency and the Nigeria central bank digital currency, which is also known as eNaira.

Granger causality test and GMM coefficient matrix methodologies were used.

There is sustained increase in global and local interest in Internet information about eNaira in the first six weeks after eNaira adoption. Local interest in Internet information about cryptocurrency in Nigeria exceeded global interest in Internet information about cryptocurrency. The south-east region had the highest interest in cryptocurrency information followed by the south-south, the north-central, the north-east, the north-west and the south-west regions. In contrast, the north-east region had the highest interest in Internet information about eNaira, followed by the north-west, the north–central, the south-west, the south-south and the south-east regions. Nigeria recorded the highest global interest in Internet information about cryptocurrency and eNaira, while Japan and Brazil recorded the lowest interest during the period. The correlation results show a significant and positive correlation between interest in cryptocurrency information and interest in eNaira information. The Granger causality results show that global interest in cryptocurrency information causes both global and local interest in eNaira information. Also, local interest in cryptocurrency information causes global interest in eNaira information. The GMM regression coefficient matrix shows a significant positive relationship between interest in cryptocurrency information and eNaira information.

There are few studies on CBDC in country-specific contexts. This study adds to the literature by examining the Nigerian context.

The purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020).

The system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg’s method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure.

In this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system.

Results of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck.

In this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed.

The goal of this study is to test the real-world use of an emotion recognition system.

The researchers chose an existing algorithm that displayed high accuracy and speed. Four emotions: happy, sadness, anger and surprise, are used from six of the universal emotions, associated by their own mood markers. The mood-matrix interface is then coded as a web application. Four guidance counselors and 10 students participated in the testing of the mood-matrix. Guidance counselors answered the technology acceptance model (TAM) to assess its usefulness, and the students answered the general comfort questionnaire (GCQ) to assess their comfort levels.

Results from TAM found that the mood-matrix has significant use for the guidance counselors and the GCQ finds that the students were comfortable during testing.

No study yet has tested an emotion recognition system applied to counseling or any mental health or psychological transactions.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.