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In this chapter, we overview different neuroenhancement techniques that could be applied for accelerating the learning process in a number of tasks that are associated with occupational roles. The techniques range from: (1) pharmaceutical and invasive methods with limited applicability to the healthy population, due to possible side effects and obtrusiveness; (2) game-based brain training that shows task-specific potential, but may not generalize; and (3) a promising new research direction in which the goal is to “train” the brain to reach an optimal cognitive state for performing a given task, and remain in this state by self-regulation. However, in order to accomplish this goal of brain training, the neurological markers that best discriminate good task performance need to be identified. We also review a number of initial studies in this chapter which have analyzed such markers in a variety of training-related applications for different occupations, such as military/security (e.g., marksmanship, deadly force judgment and decision making, submarine piloting and navigation, phishing detection), medicine (e.g., robot-assisted surgery), banking (e.g., financial traders), sports (e.g., golf, archery, and baseball), or entertainment (e.g., musicians and actors). The promising results of these early studies are fueling interest in neuroscience-based technology and methods in the rapidly developing field of organizational neuroscience (e.g., leadership research). We conclude the chapter with a discussion of future research directions.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

The present chapter demonstrates how the use of neural network software such as CATPACII^TM can aid researchers to map a vast amount of literature in order to identify emerging and established research trends. Furthermore, the use of this methodology allows for the generation of research ideas. This is particularly relevant in view of the substantially increasing number of global scholarly contributions. The utilization of the methodology is exemplified at the intersection of literature bodies in entrepreneurship, information and communication technologies (ICT), and economic development.