The phenomenon of globalization has been shaping the world economy and societies for centuries. However, a renewed interest in understanding its impact on marketing and consumers has come into the spotlight in recent years as the COVID-19 pandemic has shown how interdependent the world is. In addition, anti-globalization sentiment is growing worldwide. We believe this is a good time to take stock of the impact of globalization on businesses (especially on positioning and marketing strategies) and consumers’ responses. The aim of this monograph is to provide an overview of globalization as a phenomenon that affects business activities and consumer behavior. Upon providing a brief history of globalization and identifying the key aspects of its conceptualization, we review the impact on the emergence of a global consumer culture, the positioning and marketing strategies adopted by firms, the reactions of consumers to globalization itself (i.e., their attitudes toward it and their identity shifts) and to the diversity of products and services available in a global marketplace, and the idiosyncratic ways in which globalization affects some key industries (e.g., technology, consumer goods, and media and entertainment) and countries. We conclude with a discussion on the need for research to further understand the impact of globalization on consumption, the opportunities at the intersection of globalization and technology development, and the effects of globalization on sustainable business practices.

The rapid rise of bio-hybrid robots and hybrid human–AI systems has triggered an explosion of terminology that inhibits clarity and progress. To investigate how terms are defined, we conduct a narrative scoping review and concept analysis. We extract 60 verbatim definitions spanning engineering, human–computer interaction, human factors, biomimetics, philosophy, and policy. Entries are coded on three axes: agency locus (human, shared, machine), integration depth (loose, moderate, high), and normative valence (negative, neutral, positive), and then clustered. Four categories emerged from the analysis: (i) machine-led, low-integration architectures such as neuro-symbolic or “Hybrid-AI” models; (ii) shared, moderately integrated systems like mixed-initiative cobots; (iii) human-led, medium-coupling decision aids; and (iv) human-centric, low-integration frameworks that focus on user agency. Most definitions adopt a generally positive valence, suggesting a gap with risk-heavy popular narratives. We show that, for researchers investigating where living meets machine, terminological precision is more than semantics and it can shape design, accountability, and public trust. This narrative review contributes a comparative taxonomy and a shared lexicon for reporting hybrid systems. Researchers are encouraged to clarify which sense of Hybrid-AI is intended (algorithmic fusion vs. human–AI ensemble), to specify agency locus and integration depth, and to adopt measures consistent with these conceptualizations. Such practices can reduce construct confusion, enhance cross-study comparability, and align design, safety, and regulatory expectations across domains.