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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.

The contamination of groundwater by natural arsenic is currently a worldwide epidemic. Arsenic-contaminated groundwater has been reported in Argentina, Chile, Mexico, China, Hungary, West Bengal in India, Bangladesh, and Vietnam. Of these regions, Bangladesh and West Bengal are the most seriously affected in terms of the size of the population at risk and magnitude of health problems. Hence, chronic exposure to arsenic >50′′μg/L in drinking water can result in serious health problems. Common symptoms of arsenic-related ailments are skin, cardiovascular, renal, hematological, and respiratory disorders. Therefore, this chapter focuses on nature, origin, and extent of groundwater arsenic contamination, probable causes, and its impacts on food, drinking water, and social coverage. It further discloses mitigation approaches proposed and practiced by the different research groups to combat this problem and finally concludes.

This research aimed to determine how COVID-19 affected Micro small and medium enterprises’ (MSMEs) participation in sustainable development goals (SDGs) and their adoption of Fin-tech developing nations like India. Involving women in the workforce through microenterprises is crucial to accomplishing the SDGs, and its subsection is focused on decent work and small industries. Data related to knowledge of digital finance was collected using a convenience sample method. Nonetheless, descriptive research was conducted, and although interesting, no definitive findings could be determined. Women entrepreneurs aged between 25 and 35 years provided input into creating this link based on the United Theory of Acceptance and Use of Technology (UTAUT) concept. Using SMART-PLS, we found a significant relationship between the UTAUT model of sustainable development and the level of Fin-tech literacy among women in low-income households in developing countries like India. However, we also know that MSMEs’ adoption of Fin-tech and SDG is less tightly linked to the UTUAT model. The trend towards online transactions or digital payments and the widespread adoption of digital technology applications are changing the face of the global economy. The COVID-19 period saw the rise of contactless transactions as the primary payment method. Because of this shift in public opinion, small- and medium-sized enterprises (SMEs) have begun implementing Fin-tech for long-term growth. People worldwide use it for transactions and other economic functions. However, there is a scarcity of research incorporating this kind of data from India and the rest of the developing world.

The global economic fallout following the unexpected onset and rapid spread of COVID-19 pandemic worldwide, in early 2020, has necessitated international and national action plans towards new normal models of realignment in enterprise bottom-line and management. In 2020, ‘Supporting Small Business through the COVID-19 Crisis’ was declared the lead theme of the MSME Day – June 27 – by the UN. A ‘COVID Response Alliance for Social Entrepreneurs’ was launched by an affiliate of the World Economic Forum (WEF). Drawing inspiration from the ‘small business’ focus of the UN MSME Day declaration and the ‘social entrepreneurship’ perspective of the WEF, the study seeks to draw few perceptions and conclusions in the post-COVID economic recovery context of India, where Micro, Small and Medium Enterprises (MSMEs) are observed to be a key driver of development, thanks to an add-on supportive package in the wake of the COVID-19 economic crisis. It is found that the package fails to provide a direct push for promotion of social enterprises/entrepreneurship in the Indian MSME sector, as there is no focused policy approach on leveraging ‘entrepreneurship resources’. Hence, the general trend of the sector continues to be dominated by the ‘for-profit first’ concern rather than a fair blend of ‘social value creation first’, with ‘profit’. Discourse on social entrepreneurship and action-oriented rehabilitation tools proposed in the Covid context globally have failed to reorient the dominant outlook of social enterprises in India – business as a tool for achieving social impact – to social impact as a spontaneous/positive outcome from business. The study highlights the lapses on the ground, of theoretical formulations, despite their couching in Covid contexts, and the need for a more institutionalised enabling environment for social value creation, impact investment and social stock exchange in the social enterprise ecosystem.

In light of the data economy, data protection law is a key legal element for being able to leverage data-driven innovation and is often regarded as a limitation for businesses and service design. Contrasting this traditional view, this chapter argues why designing with privacy in mind is a win-win situation, not only, but especially in the context of data-based services. On the backdrop of new regulations around the globe setting incentives, we show how research in the domain of usable privacy can be leveraged to embed innovative privacy features for customers into digital services as competitive advantage. Building upon these insights, we argue that a well-designed privacy and/or data protection process should be a key element for customer experience management.

Purpose – Service-dominant logic (S-D logic) has conceptualized value as value-in-context where context is defined as a “set of unique actors with unique reciprocal links among them” (Chandler & Vargo, 2011, p. 40). The chapter proposes a means of measuring value-in-context as experienced by an actor while integrating resources, called the ValConRIA model (value-in-context of resource integrating activities).Design/methodology/approach – Value emerges from experiencing interactions in a service-for-service exchange. The actor perceives value as emerging with his activities and hence experiences the emerging value as connected to either his activities or the items supporting his activities or the people he is interacting with. We call these realms of experience the I (–Me) realm, the I–It and It–I realm, and the I–You and You–I realm, composing five dimensions. An exploratory principal component analysis supports this structure. The measurement process has been tested for reliability and validity and applied to different activities: using a laptop, using cigarettes (=smoking), using a smartphone, and using Facebook.Findings – According to where the actor mostly experiences the value emergence, five dimensions of value-in-context have been identified using principal component analysis. The measurement scale shows high construct reliability and discriminant validity.Implications – Being able to measure value-in-context as proposed by S-D logic brings S-D logic into practice. Practitioners can use the measurement process to identify value their customers co-create. The proposed means of measuring value-in-context does not measure the value of things but instead values as it emerges from an actor’s activities, exchanging service for service.Value/originality – To our knowledge this chapter is the first to propose a means of measuring value-in-context, which is based on S-D logic.