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This chapter explores the ethical challenges arising from the integration of advanced artificial intelligence (AI) technologies into intelligent manufacturing systems. Machine learning (ML), augmented reality/virtual reality (AR/VR), digital twins, and human–robot collaboration (HRC) redefine industrial production, they bring forth unprecedented efficiencies and capabilities but also introduce complex ethical considerations. The text delves into issues such as data privacy, job displacement, the impact of automation on workforce dynamics, and the psychological effects of working alongside AI-powered systems. Through a detailed examination of these technologies and their implications, the chapter advocates for a dynamic ethical framework that evolves alongside technological advancements. This framework should prioritize human dignity, safety, and rights, involving all stakeholders in its development and implementation. By addressing the ethical implications of AI, AR/VR, digital twins, and HRC, the chapter underscores the necessity of balancing technological innovation with ethical responsibility. It calls for collaborative efforts involving policymakers, industry leaders, workers, and consumers to navigate the ethical landscape of intelligent manufacturing, aiming to harness the potential of these technologies responsibly for the betterment of society and the workforce.

This paper aims to extend the literature on social proof by looking at the effectiveness of social proof on behaviour change for environmental benefit.

The research is based on real case studies currently intended to encourage behaviour change among residents of a large UK city. An initial study assesses the motivation displayed within each case study. A second study then examines whether recipients recognise their own motivation in each case study.

Results indicate that participants did not recognise their own motivation in the case studies that were expected to be most similar to them, suggesting that recipients do not recognise “social proof” according to motivation. However, a relationship is observed between recipients’ gender and the gender of the case studies.

Demographics appear to be a better basis for social proof than motivation. This paper recommends several future avenues for further exploration, including using case studies that represent a wider range of characteristics (such as demographics). The current range of stimulus materials is limited, as these are real materials currently being used in a large UK city.

The results indicate that portraying motivation is not a good basis for using the social proof principle. Instead, social marketers ought to focus on representing similarity to the intended audience based on other characteristics such as gender.

The research contributes a new direction in this field, using Self-determination Theory to match social proof examples to recipients.