Purpose: This paper aims to contribute to the discussion on integrating humans and technology in customer service within the framework of Society 5.0, which emphasizes the growing role of artificial intelligence (AI). It examines how effectively new generative AI-based chatbots can handle customer emotions and explores their impact on determining the point at which a customer–machine interaction should be transferred to a human agent to prevent customer disengagement, referred to as the Switch Point (SP).Design/methodology/approach: To evaluate the capabilities of new generative AI-based chatbots in managing emotions, ChatGPT-3.5, Gemini and Copilot are tested using the Trait Emotional Intelligence Questionnaire Short-Form (TEIQue-SF). A reference framework is developed to illustrate the shift in the Switch Point (SP).Findings: Using the four-intelligence framework (mechanical, analytical, intuitive and empathetic), this study demonstrates that, despite advancements in AI’s ability to address emotions in customer service, even the most advanced chatbots—such as ChatGPT, Gemini and Copilot—still fall short of replicating the empathetic capabilities of human intelligence (HI). The concept of artificial emotional awareness (AEA) is introduced to characterize the intuitive intelligence of new generative AI chatbots in understanding customer emotions and triggering the SP. A complementary rather than replacement perspective of HI and AI is proposed, highlighting the impact of generative AI on the SP.Research limitations/implications: This study is exploratory in nature and requires further theoretical development and empirical validation.Practical implications: The study has only an exploratory character with respect to the possible real impact of the introduction of the new generative AI-based chatbots on collaborative approaches to the integration of humans and technology in Society 5.0.Originality/value: Customer Relationship Management managers can use the proposed framework as a guide to adopt a dynamic approach to HI–AI collaboration in AI-driven customer service.

The hydrophobic effect is the main factor that drives the folding of polypeptide chains. In this study, we have examined the influence of the hydrophobic effect in the context of the main mechanical forces approach, mainly in relation to the establishment of specific interplays, such as hydrophobic and CH–π cloud interactions. By adopting three oligopeptides as model systems to assess folding features, we demonstrate herein that these finely tuned interactions dominate over electrostatic interactions, including H-bonds and electrostatic attractions/repulsions. The folding mechanism analysed here demonstrates cooperation at the single-residue level, for which we propose the terminology of “single residues cooperative folding”. Overall, hydrophobic and CH–π cloud interactions produce the main output of the hydrophobic effect and govern the folding mechanism, as demonstrated in this study with small polypeptide chains, which in turn represent the main secondary structures in proteins.

The genetic material in living systems is mainly stored in DNA molecules, which in turn play a dominant biological role in relation to the coding and transfer of genetic information, the biosynthesis of proteins and RNA and the packaging and regulation of DNA expression and accessibility. These features, strictly dictated by the three-dimensional structure of DNA, are governed by non-covalent chemical interactions that drive the folding process of these biological macromolecules. The Main Mechanical Forces (MMFs) approach is a recently formulated calculation method, based on the accurate prediction of structural features of biomolecules through an in-depth assessment of the interplay between specific non-covalent chemical interactions and related mechanical forces developed during the folding process. By adopting the MMFs method in the context of nucleic acids, we report here the results obtained in terms of predicting three-dimensional DNA oligomer tertiary structures. To this end, we have developed tailored nucleic acid-specific equations, enabling to predict the torsion angles (with a relevant level of agreement with experimental values) of the phosphate-sugar backbone of the three model molecules A-, B- and Z- DNA used in this study. To increase the validity of this methodology, we have conducted RMSD measurements, indicating that there is a weak but rather acceptable match between the calculated vs. predicted A-DNA structure, whereas the prediction of the BII-DNA and Z-DNA tertiary structures was fully correct.