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Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Many experts agree with the opinion that blockchain technology will bring about a revolution and create a new system of living, relating and doing business. What so far seems to be an experimental phenomenon, which is already producing results and creating high-value communities, may soon become a real social transformation. The case study presented here sheds some light on the blockchain reality, the importance of digital entrepreneurial talent, which seeks to take advantage of a situation in the country to develop a commercial project attractive to foreign investors. The authors analyze the key elements of the success of a Paraguayan company focused on high-tech operations. The authors venture to present an exploratory model of management in the blockchain world, in order to collaborate, cooperate and encourage the success of many future digital ventures.

The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

This chapter is a speculative examination of the way in which theory is used in the social accounting literature. It is intended as an initial guide for those approaching the area for the first time; it is intended as a map for those in the field who would like to adopt a bigger picture for their work and it is intended as a wake-up call to those of us stuck in unconscious ruts. The chapter's departure point is the recognition that social accounting requires a reasonably sophisticated awareness of theory – not least because the subject matter itself is so contentious and conditional. The chapter's ambitions are to encourage a more evaluative and policy-based approach to the subject matter of social accounting; to offer a pedagogic basis to help others make some sense of theory in social accounting and to seek to empower and liberate social accounting scholars by assisting the range of their theorising. Social accounting scholars tend to approach the area with concerns and desires for liberation and possibility. Theory can help articulate those concerns and can support and encourage that desire. Above all, the chapter is explicitly partial, speculative and tentative, and it is not a formal or informed attempt to produce a theory of theories in social accounting. To articulate a range of the possible theories, we offer a simple heuristic through which we may navigate our way through the soup of concepts and perceptions that can blend into a potential infinity of ways of looking and seeing. We hope to encourage diversity and speculation rather than narrowness and alleged certainty.

The authors measure the impact of culture on Sharia; Social and Financial Disclosure (SSFD) of Islamic Banks (IBs) around the world.

Content analysis is used to measure levels of disclosure for a sample of 136 IBs of 25 countries for years 2013 and 2014. Different cultural measures are used. These include secrecy/transparency as suggested by Gray (1988) and Hofstede (1980, 1983, 2001, 2010)’s culture dimensions which include: Power Distance; Individualism; Masculinity; Uncertainty Avoidance; Long-Term Ordination and Indulgence. Ordinary least square (OLS) regression is used to test the research hypotheses.

After controlling bank-specific, corporate governance and country characteristics, the authors found that Hofstede’s culture dimensions have a significant impact on SSFD. They also found that Gray's transparency dimension positively influence levels of sharia, social and aggregated disclosure. Therefore, they conclude that culture influences levels of disclosure in IBs.

This study has policy implications for managers and regulators of Islamic banking industry.

This study is the first to use both Gray and Hofstede models in the context of IBs around the world. It also the first to explore the impact of culture on three different disclosure levels for IBs.

This chapter intends to contribute to the debate on the determinants of corporate social responsibility (CSR) and their impact on performance measurement and communication systems. It aims at analyzing the relationship between the reasons why firms adopt CSR and the importance given to voluntary CSR disclosure.

Two main categories of CSR determinants have been identified: the external ones, coming from the environment outside the firm, and the internal determinants, which are linked to some specific characteristics of the enterprise and to the objectives it pursues.

The analyzed sample consists of 120 large Italian manufacturing and nonmanufacturing enterprises. The research hypotheses concerning the relationship between external and internal determinants of CSR and CSR disclosure were verified using an independent sample t-test, evaluating the equal variances of clusters using the Levene’s test.

Main results point out that in companies giving importance to CSR disclosure, the internal drivers are more relevant than the external ones in determining the attitude toward CSR. Among the internal determinants, drivers related to company and management values and ethics are quite relevant.

This study is subject to the limitations that generally apply to cross-sectional survey-based research.

Our research findings show that legitimacy theory represents the most relevant theory in explaining CSR disclosure practices of Italian large firms, as well as the operational implementation of stakeholder theory, such as stakeholder management. On the contrary, institutional theory only partially explains CSR disclosure, with respect to the pressures coming from financial markets.

Neuroscience research on human motivation in the workplace is still in its infancy. There is a large industrial and organizational (IO) psychology literature containing numerous theories of motivation, relating to prosocial and productive, and, less so, “darker” antisocial and counter-productive, behaviors. However, the development of a viable over-arching theoretical framework has proved elusive. In this chapter, we argue that basic neuropsychological systems related to approach, avoidance, and their conflict, may provide such a framework, one which we discuss in terms of the Reinforcement Sensitivity Theory (RST) of personality. We argue that workplace behaviors may be understood by reference to the motivational types that are formed from the combination of basic approach, avoidance, and conflict-related personalities. We offer suggestions for future research to explore workplace behaviors in terms of the wider literature on the neuroscience of motivation.

From well before the mid-19th up to the mid-20th century those scholars who read and commented on The Essential Principles of the Wealth of Nations, including Marx and Seligman, seem to have been unaware of the very name of its author. Since then it has become accepted knowledge (again) that the work was written by one John Gray. Beyond the name, however, biographical details about Gray have remained extremely sparse until the present day. If one were to use a measure of obscurity, something which perhaps is appropriate in a work devoted to ‘neglected economists’, then one may use the fact that neither the Oxford Dictionary of National Biography (old or new editions), nor the Palgrave Dictionary of Economics (any edition), nor any other biographical dictionaries devote an entry to Gray. The modern authors who discuss his economic writings contend themselves with the statement that ‘little biographical information is available about Gray’ (Delmas & Demals, 1995, p. 119, n. 5).¹ This is unfortunate because at least some knowledge about the personal background and career of an author is often useful in arriving at a better understanding of his or her ideas. This, as will become clear shortly, is the case too for John Gray.