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The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.

This study aims to investigate the merits of the unified model of task-specific motivation (UMTM) in predicting transfer of training and to investigate (relationships between) changes in UMTM components over time. In doing so, this study takes the multidimensionality of transfer motivation into account.

The authors collected data among 514 employees of the judiciary who filled in the UMTM questionnaire directly after the training and after three weeks. The data were analyzed by means of structural equation modelling.

The outcomes show that transfer motivation predicts transfer intention and transfer of training over time. Moreover, the study shows that (change in) transfer motivation is predicted by (change in) personal and contextual factors identified by the UMTM as antecedents of motivation.

This study describes the first longitudinal evaluation of the UMTM in the literature and shows its applicability for predicting transfer of training. It is also one of the few studies that investigate transfer motivation multidimensionally and the role it plays for transfer of training. As such, this study informs other transfer of training models about the nature of transfer motivation and how transfer of training could be predicted.

The purpose of this paper is to study the concept of tax gap, that is the difference between the total amount of taxes collected and the total tax revenues that would be collected under full tax compliance.

The authors also present the methodology to estimate the gap for two taxes levied on wealth: the wealth tax and the inheritance and gift tax; both are administered in Spain by the regional tax authorities.

The authors point out that its estimation offers useful information about the relative size and nature of non-compliance, as well as its evolution over time. Likewise, the tax gap is a valuable instrument not only to define enforcement strategies of the tax administration but also to enhance its accountability. Nonetheless, the methodology used to estimate the tax gap and, consequently, the interpretation of the results is subject to limitations that are discussed in the paper.

Finally, the paper provides the results of the estimations obtained from using microdata: 44.34 per cent gap in the wealth tax and 41.26 per cent in the inheritance and gift tax.

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

In many UK mental health services, in-patient psychiatric care is being separated from community care by having dedicated in-patient medical team. We evaluated staff satisfaction in this functionalised in-patient care. A survey was conducted amongst multidiscipli-nary staff from various teams using a questionnaire survey. On an average 14.3% of staff returned a satisfactory response for function-alisation, 57.3% had unsatisfactory response and others were undecided or perceived no change. There was no difference in responses amongst age, gender and professional groups. Mean scores of all groups were within unsatisfactory domain; however community staff compared to in-patient staff and staff with more than 5 years of experience compared to those with 1-5 years of experience returned significantly more unsatisfactory responses regarding functionalisation. Many positive and negative aspects of functionalisation were raised. The results of this evaluation suggest the need for further studies on the effectiveness of in-patient functionalisation. Short and long term clinical outcomes and the satisfaction of the patients should also be studied.

Since the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.

To explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.

The results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).

To the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Ethics is fundamentally a science of social and collective responsibility. Ethics concerns human behavior as responsible or accountable. Because of the nature of social interaction, certain members of the society will bear greater authority, and hence, greater individual and social responsibility than others. In our world, personal responsibility and social responsibility are hardly separable. Personal responsibility becomes responsibility for the world because the person and the world are inseparable. In this chapter, we use the term responsibility from a legal, ethical, moral, and spiritual (LEMS) standpoint as some promise, commitment, obligation, sanctioned by self, morals, law, or society, to do good, and if harm results, to repair harm done on another. Hence, responsibility from a moral perspective is trustworthiness and dependability of the agent in some enterprise. Its inverse is exoneration – the extent to which one is excused from commitment and repairing the harm done to others by one’s actions. We apply the theories and constructs of executive responsibility to two contemporary cases: (1) India’s Super Rich in 2014 and (2) the Fall and Rise of Starbucks. After exploring the basic notion of responsibility, we present a discussion on the nature and obligation of corporate responsibility into three parts: Part I: Classical Understanding and Discussion on Corporate Responsibility; Part II: Contemporary Understanding and Discussion on Corporate Responsibility, and Part III: A synthesis of classical and contemporary views of responsibility and their applications to corporate executive responsibility.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The purpose of this paper is to analyze empirically the relationship between the labor share and income inequality, as measured by the Gini coefficient and by the income shares for different quintiles, during the period 1990–2015 for 62 developed and developing countries.

This study uses panel data techniques to analyze empirically the relationship between the labor share and income inequality.

This paper finds that a lower labor share is associated with a higher Gini coefficient. A lower labor share is found to be strongly associated with a smaller income share for the lowest two quintiles and larger income share for the highest quintile and weakly associated with a smaller income share for the third and fourth quintiles. Moreover, this paper finds that the lower the quintile, the stronger the impact of the labor share on the income share of the quintile.

Policymakers should take into account the evolution of the labor share. Public policies that improve labor market outcomes, such as those aimed to promote participation in the labor market and strengthen the human capital of low-income groups, seem necessary to prevent the rise in economic inequalities. Moreover, as the digital transformation of society progresses, policies to promote skill deepening may have an important role in reversing excessive inequalities.

How changes in the labor share are associated with changes in the Gini coefficient, and how this is driven by income shares for different quintiles, for a broad range of countries during the most recent period, has not been comprehensively studied using panel data techniques.