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Additive manufacturing (AM) has the potential to transform the organisation of all the activities carried out by firms. The growing diffusion of these technologies is increasingly challenging multinational enterprises to reinvent their businesses. Accordingly, many scholars argue that AM may reduce countries’ participation in global value chains (GVCs) or, at least, affect GVCs’ geography, length and further developments. However, so far, the lack of available data on the real worldwide diffusion of these technologies has precluded the possibility to study this phenomenon from an empirical standpoint.

This study investigates AM technologies, with a particular focus on their possible impact on GVCs, in the framework of the current debate in international business. In order to examine this relationship and overcome the lack of adoption data, the authors identify a potential proxy of AM diffusion – that is, patenting activity. Coherently, the authors employ this proxy and a country-level measure of GVC participation (i.e., the Share of Re-Exported Inputs on Total Imported Inputs) to empirically investigate the role of AM in influencing countries’ participation to GVCs. This country-level analysis is focussed on three specific industries and the aggregate economy in 58 countries for the period 2000–2014.

The results show that AM decreases a country’s participation in GVCs, both at the country level and, in particular, in the sectors which are more likely to be affected by AM technologies. This evidence suggests that this phenomenon might be induced by a decreasing reliance on intermediates processed abroad, hence an increasing importance of domestic goods, manufactured via AM.

The purpose of this paper is to present a conceptual framework that provides insights into major environmental and organizational forces underlying greater levels of organizational responsiveness to the environmental sustainability imperative by a growing number of firms, worldwide.

The paper is conceptual in its focus, and the proposed framework builds on extant literature from multiple literature streams.

Societal progress toward environmental sustainability is a shared responsibility of consumers, corporations, and the government at various levels. A potential avenue for societal progress toward environmental sustainability is fostering a macroenvironment that is conducive to the elimination of consumption certain products, reduction in consumption certain other products, and redirection of consumption of still other products from ecologically more harmful to ecologically less harmful substitute products (and relatedly, demand elimination, demand reduction, and demand redirection).

An implication for corporate sustainability responsibility is that firms while planning and formulating strategies for increasing their market footprint must also concurrently plan and formulate strategies for decreasing their environmental footprint. An implication for government sustainability responsibility is that even under conditions of high levels of commitment by a large and growing number of firms and consumers to engage in environmentally sustainable behaviors, in the absence of supporting infrastructure for engaging in such behavior, they may find it necessary to engage in environmentally unsustainable behaviors.

Issues relating to environmental sustainability have been the focus of a large body of recent research in a number of academic disciplines including marketing. A cursory examination of numerous articles published in scholarly journals on issues pertaining to environmental sustainability, and in the business press pertaining to the myriad environmental sustainability initiatives of firms worldwide is indicative of its growing importance.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

Lester and Wolff (2013) find little empirical support for the Austrian business cycle theory. According to their analysis, an unexpected monetary shock does not alter the structure of production in a way consistent with the Austrian view. Rather than increasing production in early and late stages relative to middle stages, they find the opposite – a positive monetary shock typically decreases production in early and late stages relative to middle stages. We argue that the measures of production and prices employed by Lester and Wolff (2013) are constructed in such a way that makes them inappropriate for assessing the empirical relevance of the Austrian business cycle theory’s unique features. After describing how these measures are constructed and why using ratios of stages is problematic, we use a structural vector autoregression to consider the effects of a monetary shock on each stage of the production process. We show that, with a clearer understanding of what is actually being measured by the stage of process data, the results are consistent with (but not exclusive to) the Austrian view.

The chapter investigates the relationship between environmental management practices (EMPs) and financial performance (FP).

The study is based on a sample of 187 SMEs and uses data on six EMPs (energy, water, waste, material, emissions, and biodiversity) collected through a self-administered questionnaire from owner-managers of SMEs. Ordinary least squares regression is employed to model the hypothesized paths.

The results suggest a positive and significant relationship between EMPs (energy, water, and material) and FP. There is also a significant positive relationship between an aggregate EMP measure and FP. However, other EMPs (waste, emissions, and biodiversity) are not significantly associated with FP. Overall, these results provide empirical support to the mostly normative suggestion that the conflicting results on the environmental management and financial performance relationship are partly due to the EMP measure used.

The study is based on cross-sectional data, and therefore, it is impossible to determine any changes over time. Longitudinal studies could help confirm the relationship between EMP and FP over a longer period. From a policy perspective, this results mean that the Ghanaian EPA must monitor more closely for violations of laws and regulations relating to waste, emissions, and biodiversity since SMEs do not have incentives to manage these impacts without commensurate return.

The study contributes by documenting evidence of the relationship between multiple measures of EMP and FP. This unlike most existing studies has enabled us to report evidence of how each EMP measure affects FP differently and where win–win opportunities are for SMEs. Thus, the win–win opportunities are associated with some EMP measures but not all.