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Purpose – Electric vehicles are very topical in developed countries. The breakthrough of new battery technologies and changing conditions driven by climate policy and growing fossil fuel prices has caused all major car manufacturing countries in the developed world to initiate R&D programmes to gain competitive advantage and to foster market diffusion of electric vehicles (EVs). This chapter looks at developments in China and compares them with observations from developed countries to draw conclusions about differences in their future paths of development.

Methodology – This chapter escribes the potentials and R&D approaches for different types of EVs in developing countries, using China as example, in comparison with developed countries. It looks at innovation strategies, policy framework and potential diffusion of EVs.

Findings – Market diffusion strategies in developed countries and China may differ, since, in the former manufacturers try to implement a premium strategy (i.e. offer high-price sophisticated EVs), while in the latter market, diffusion will probably appear at the lower end of vehicle types, i.e. via electric scooters and small urban vehicles. It is concluded that the market introduction strategies of EVs in developing countries and developed countries could converge because signs of downsizing of vehicles can be observed in the developed world, while upscaling from bikes and electric scooters can be expected for China, so that large-scale market introduction could occur via small city cars.

Implications for China – Instead of following the Western motorisation path, an option for China could be to develop a new one-stop-shop mobility concept integrating small EVs into such a concept.

Machine learning and artificial intelligence (AI) have arisen as the availability of larger data sources, statistical methods, and computing power have rapidly and simultaneously evolved. The transformation is leading to a revolution that will affect virtually every industry. Businesses that are slow to adopt modern data practices are likely to be left behind with little chance to catch up.

The purpose of this chapter is to provide a brief overview of machine learning and AI in the business setting. In addition to providing historical context, the chapter also provides justification for AI investment, even in industries in which data is not the core business function. The means by which computers learn is de-mystified and various algorithms and evaluation methods are presented. Lastly, the chapter considers various ethical and practical consequences of machine learning algorithms after implementation.

The most commonly described components of customer experience include cognitive and affective aspects. However, the subjective self-reported methods traditionally applied in tourism research cannot fully represent the instant, dynamic, and affective nature of customer experience. Therefore, there is a need for moment-based approaches and longitudinal methods in tourism research. The chapter provides a selective review of measures that can be used to assess the affective aspects of customer experience. Taking into account the advantages and limitations of each method, the integration of self-reported scales, moment-based psychophysiological techniques, and longitudinal methods should be considered as the best approach to measuring affective components of customer experience in tourism. This holistic interdisciplinary approach will help researchers and tourism practitioners understand the relationship between affective and cognitive components of tourists' pre-visit, onsite, and post-visit experience, as well as evaluate the effectiveness of marketing campaigns, identify weak points of tourists' customer journey, and maximize total travel experience.

Purpose – The shipping industry is generally recognised as having better fuel efficiency than other transport modes. In many regions of the world, therefore, policy has promoted shipping as the preferred freight transport mode of choice. In recent years, however, environmental problems associated with shipping have emerged. Several influential analyses have revealed the impact of shipping on air quality, particularly in the form of emissions of sulphur, nitrogen oxides and particulate matter, all of which have adverse consequences for human health.

Methodology/approach – An extensive environmental profile of shipping is provided, focusing specifically on the atmospheric pollution that is directly attributable to shipping operations.

Findings – It is important, however, to place the environmental profile of the shipping industry into the context of exactly how much transport work it does. This makes it clear that where shipping is a viable modal alternative then, in relative terms and most contexts, it still retains significant environmental advantages over other modes. The industry and its regulators have been slow, however, to improve its environmental profile and maintain its inherent advantage. Technical and operational measures which the industry may implement unilaterally are analysed, but these are deemed insufficient to stem the adverse tide of environmental concerns. Regulation is a necessity. Recently implemented regulatory measures are analysed, together with possible scenarios for the future regulation of greenhouse gas emissions. The IMO approach of global regulation is supported in preference to regionally based regulatory policies. There is also a danger that regulatory intervention may distort mode choice contexts.

Originality/value – The provision of an extensive environmental profile of shipping and an examination of this profile in relation to the importance of this transport mode to the global economy.

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.