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This paper examined tax compliance and its impact on agrochemical traders in Ghana.

Based on the registered agrochemical lists obtained from the Plant Protection and Regulatory Service Department, 92 agrochemical traders were sampled for data collection. Probit regression was used to estimate determinants of tax compliance, whereas the Inverse Probability Weighted Regression Adjustment Model was employed to evaluate the impact of tax compliance on business performance.

The results revealed that age and gender relate positively to enforced tax compliance, while education positively impacts voluntary tax compliance. Nonetheless, tax rate, trust and monthly sales positively affect voluntary tax compliance but negatively impact enforced tax compliance. Inversely, while authorities’ power negatively impacted voluntary compliance, it positively influenced enforced tax compliance confirming the Slippery Slope Framework.

To the best knowledge of the authors, this paper is the first to investigate tax compliance determinants and impact among agrochemical traders, despite the tremendous growth of the agrochemical sub-sector in Africa and Ghana. Therefore, this study makes a modest contribution to empirical studies that validate the Slippery Slope Framework in promoting tax compliance in the agricultural and agribusiness sectors of a developing country. Similarly, it also unearths the impact of tax compliance on agribusiness growth which has yet to be highlighted in the extant literature.

This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.

The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.

The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.

Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.

This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

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.

This paper aims to tackle an important question related to women’s economic empowerment in highly patriarchal societies like Egypt. The paper discusses individual, household, wealth and location factors determining women empowerment, as measured by two dimensions: decision-making power and mobility.

Using the “Egypt Labor Market Panel Survey” (ELMPS) 2012, a Multiple Indicators Multiple Causes (MIMIC) model was estimated to study the main economic resources and social constraints that determine women empowerment as measured by the power of women over household decisions and her freedom of movement in Egypt.

Three key messages could be delivered. First, women’s own economic resources as captured by her employment status are an important source of her empowerment. Second, contrary to theoretical prediction education is not playing its expected role in developing awareness and transforming ideas concerning gender roles in Egypt. Third, the importance of social local context is fundamental for Egyptian women empowerment.

This study is an attempt to address some of the gaps in the literature for the Egyptian case, where there is a lack in rigorous studies measuring women empowerment and examining its determinates. This is done by first, tackling multiple dimension of women’s empowerment, decision-making inside households and freedom of mobility. Second, using MIMIC model, which is a modeling approach that allows for studying the relations between several causes of a given latent variable, such as “Empowerment” in our case, and a number of its possible indicators, without a directly observable measure of the latent variable. Third, using the most recent set of data; the ELMPS 2012 which has a special focus on women’s resources and agency that permits greater content validity of the multidimensional setup. Forth, the macro level differences in women’s status are tackled through using location dummy variables. Finally, given the important correlation between wealth level and women empowerment, the paper is considered a first attempt to analyze such impact by including a variable that captures the wealth level of the woman’s household as one determinant of empowerment.

Global value changes continued to expand until the late 2000s. On the other hand, regional value chains have formed around major regional hubs due to the expansion of domestic demand in emerging economies, such as China, and strengthened trade protectionism since the global financial crisis. Such changes lead to the reorganisation of value chains, focusing on domestic markets (reshoring) or neighbouring countries (nearshoring). In particular, the importance of supply chain risk management has been highlighted following disruptions to the supply network due to the COVID-19 outbreak in December 2019. In this regard, major countries such as the USA and the EU are rapidly shifting to regional value chains for stable and sustainable production, rather than primarily aiming for production efficiency targeted at reducing costs. Industries in particular are more exposed to such supply chain risks under the existing structure and it now has become extremely important for businesses to take reaction to such risks. This is especially important for major industries in a country such as automobile or semiconductor manufacturing industries in South Korea. The aim of this study, therefore, is to establish the basis for the simultaneous growth of ports and linked industries by examining the existing structure of the global value chain for the automotive industry, which has a strong presence in South Korea’s domestic economy. In this regard, this research carries out a supply chain analysis focusing on the imports and exports of automotive parts. It also analyses the current structural risks and suggests risk management measures to secure a stable supply chain.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The purpose of the paper is to propose an effective approach of artificial intelligence (AI) addressing social-humanitarian reality comprising non-formalizable representation. The new task is to describe processes of integration of AI and humans in the hybrid systems framework.

Social-humanitarian dynamics contradict traditional characteristics of AI. Suggested methodology embraces formalized and non-formalized parts as a whole. Holonic and special convergent approaches are combined to ensure purposefulness and sustainability of collective decision-making. Inverse problem solving on topology spaces, control thermodynamics and non-formalizable (considering quantum and relativistic) semantics include observers of eigenforms of reality.

Collective decision-making cannot be represented only by formal means. Thus, this paper suggests the equation of hybrid reality (HyR), which integrates formalizable and non-formalizable parts conveying and coalescing holonic approaches, thermodynamic theory, cognitive modeling and inverse problem solving. The special convergent approach makes the solution of this equation purposeful and sustainable.

The suggested approach is far reaching with respect of current state-of-the-art technology; medium-term limitations are expected in the creation of cognitive semantics.

Social-humanitarian events embrace all phenomena connected with individual and collective human behavior and decision-making. The paper will impact deeply networked experts, groups of crowds, rescue teams, researchers, professional communities, society and environment.

New possibilities for advanced AI to enable purposeful and sustainable social-humanitarian subjects. The special convergent information structuring during collective decision-making creates necessary conditions toward the goals.

The purpose of this paper is to make the case for bringing compassion to students in educational settings, preschool through graduate school (PK-20).

First, the author defines what is meant by “compassion” and differentiates it from the related constructs. Next, the author discusses the importance of bringing compassion into education, thinking specifically about preschool, K-12 (elementary and middle school/junior high/high school), college students, and graduate students (e.g. law, medical, nurses, counselors and therapists-in-training). The author then reviews the scant empirical literature on compassion in education and makes recommendations for future research. In the final section, the author makes specific and practical recommendations for the classroom (e.g. how to teach and evaluate compassion in PK-20).

While there is a fair amount of research on compassion with college students, and specifically regarding compassion for oneself, as the author reviews in this paper, the field is wide open in terms of empirical research with other students and examining other forms of compassion.

This is not a formal review or meta-analysis.

This paper will be a useful resource for teachers and those interested in PK-20 education.

This paper highlights the problems and opportunities for bringing compassion into education settings.

To date, no review of compassion in PK-20 exists.

In this article we aim to understand how the network formed by fitness tracking devices and associated apps as a subset of the broader health-related Internet of things is capable of spreading information.

The authors used a combination of a content analysis, network analysis, community detection and simulation. A sample of 922 health-related apps (including manufacturers' apps and developers) were collected through snowball sampling after an initial content analysis from a Google search for fitness tracking devices.

The network of fitness apps is disassortative with high-degree nodes connecting to low-degree nodes, follow a power-law degree distribution and present with low community structure. Information spreads faster through the network than an artificial small-world network and fastest when nodes with high degree centrality are the seeds.

This capability to spread information holds implications for both intended and unintended data sharing.

The analysis confirms and supports evidence of widespread mobility of data between fitness and health apps that were initially reported in earlier work and in addition provides evidence for the dynamic diffusion capability of the network based on its structure. The structure of the network enables the duality of the purpose of data sharing.